{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/02_pytorch_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r8C1WSzsHC7x"
   },
   "source": [
    "# 02. PyTorch Neural Network Classification\n",
    "\n",
    "## What is a classification problem?\n",
    "\n",
    "A [classification problem](https://en.wikipedia.org/wiki/Statistical_classification) involves predicting whether something is one thing or another.\n",
    "\n",
    "For example, you might want to:\n",
    "\n",
    "| Problem type | What is it? | Example |\n",
    "| ----- | ----- | ----- |\n",
    "| **Binary classification** | Target can be one of two options, e.g. yes or no | Predict whether or not someone has heart disease based on their health parameters. |\n",
    "| **Multi-class classification** | Target can be one of more than two options | Decide whether a photo of is of food, a person or a dog. |\n",
    "| **Multi-label classification** | Target can be assigned more than one option | Predict what categories should be assigned to a Wikipedia article (e.g. mathematics, science & philosohpy). |\n",
    "\n",
    "![various different classification in machine learning such as binary classification, multiclass classification and multilabel classification](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-different-classification-problems.png)\n",
    "\n",
    "Classification, along with regression (predicting a number, covered in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)) is one of the most common types of machine learning problems.\n",
    "\n",
    "In this notebook, we're going to work through a couple of different classification problems with PyTorch. \n",
    "\n",
    "In other words, taking a set of inputs and predicting what class those set of inputs belong to.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9wTlTaDKH7Oj"
   },
   "source": [
    "## What we're going to cover\n",
    "\n",
    "In this notebook we're going to reiterate over the PyTorch workflow we coverd in notebook 01.\n",
    "\n",
    "![a pytorch workflow flowchart](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/01_a_pytorch_workflow.png)\n",
    "\n",
    "Except instead of trying to predict a straight line (predicting a number, also called a regression problem), we'll be working on a **classification problem**.\n",
    "\n",
    "Specifically, we're going to cover:\n",
    "\n",
    "| **Topic** | **Contents** |\n",
    "| ----- | ----- |\n",
    "| **0. Architecture of a classification neural network** | Neural networks can come in almost any shape or size, but they typically follow a similar floor plan. |\n",
    "| **1. Getting binary classification data ready** | Data can be almost anything but to get started we're going to create a simple binary classification dataset. |\n",
    "| **2. Building a PyTorch classification model** | Here we'll create a model to learn patterns in the data, we'll also choose a **loss function**, **optimizer** and build a **training loop** specific to classification. | \n",
    "| **3. Fitting the model to data (training)** | We've got data and a model, now let's let the model (try to) find patterns in the (**training**) data. |\n",
    "| **4. Making predictions and evaluating a model (inference)** | Our model's found patterns in the data, let's compare its findings to the actual (**testing**) data. |\n",
    "| **5. Improving a model (from a model perspective)** | We've trained an evaluated a model but it's not working, let's try a few things to improve it. |\n",
    "| **6. Non-linearity** | So far our model has only had the ability to model straight lines, what about non-linear (non-straight) lines? |\n",
    "| **7. Replicating non-linear functions** | We used **non-linear functions** to help model non-linear data, but what do these look like? |\n",
    "| **8. Putting it all together with multi-class classification** | Let's put everything we've done so far for binary classification together with a multi-class classification problem. |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uxdUc9OfHtgU"
   },
   "source": [
    "## Where can can you get help?\n",
    "\n",
    "All of the materials for this course [live on GitHub](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "And if you run into trouble, you can ask a question on the [Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) there too.\n",
    "\n",
    "There's also the [PyTorch developer forums](https://discuss.pytorch.org/), a very helpful place for all things PyTorch. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MSLHJiQxH4jU"
   },
   "source": [
    "## 0. Architecture of a classification neural network\n",
    "\n",
    "Before we get into writing code, let's look at the general architecture of a classification neural network.\n",
    "\n",
    "| **Hyperparameter** | **Binary Classification** | **Multiclass classification** |\n",
    "| --- | --- | --- |\n",
    "| **Input layer shape** (`in_features`) | Same as number of features (e.g. 5 for age, sex, height, weight, smoking status in heart disease prediction) | Same as binary classification |\n",
    "| **Hidden layer(s)** | Problem specific, minimum = 1, maximum = unlimited | Same as binary classification |\n",
    "| **Neurons per hidden layer** | Problem specific, generally 10 to 512 | Same as binary classification |\n",
    "| **Output layer shape** (`out_features`) | 1 (one class or the other) | 1 per class (e.g. 3 for food, person or dog photo) |\n",
    "| **Hidden layer activation** | Usually [ReLU](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html#torch.nn.ReLU) (rectified linear unit) but [can be many others](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions) | Same as binary classification |\n",
    "| **Output activation** | [Sigmoid](https://en.wikipedia.org/wiki/Sigmoid_function) ([`torch.sigmoid`](https://pytorch.org/docs/stable/generated/torch.sigmoid.html) in PyTorch)| [Softmax](https://en.wikipedia.org/wiki/Softmax_function) ([`torch.softmax`](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) in PyTorch) |\n",
    "| **Loss function** | [Binary crossentropy](https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_loss_function_and_logistic_regression) ([`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) in PyTorch) | Cross entropy ([`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) in PyTorch) |\n",
    "| **Optimizer** | [SGD](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) (stochastic gradient descent), [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) (see [`torch.optim`](https://pytorch.org/docs/stable/optim.html) for more options) | Same as binary classification |\n",
    "\n",
    "Of course, this ingredient list of classification neural network components will vary depending on the problem you're working on.\n",
    "\n",
    "But it's more than enough to get started.\n",
    "\n",
    "We're going to gets hands-on with this setup throughout this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VwvxFEjKHC71"
   },
   "source": [
    "## 1. Make classification data and get it ready\n",
    "\n",
    "Let's begin by making some data.\n",
    "\n",
    "We'll use the [`make_circles()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html) method from Scikit-Learn to generate two circles with different coloured dots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "RGeZvHsyHC72"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "\n",
    "# Make 1000 samples \n",
    "n_samples = 1000\n",
    "\n",
    "# Create circles\n",
    "X, y = make_circles(n_samples,\n",
    "                    noise=0.03, # a little bit of noise to the dots\n",
    "                    random_state=42) # keep random state so we get the same values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1FwwzJnQV2jv"
   },
   "source": [
    "Alright, now let's view the first 5 `X` and `y` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oAb8vcIhWEO8",
    "outputId": "b7316d88-7733-4981-9b4a-0a98c7cdd829"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 X features:\n",
      "[[ 0.75424625  0.23148074]\n",
      " [-0.75615888  0.15325888]\n",
      " [-0.81539193  0.17328203]\n",
      " [-0.39373073  0.69288277]\n",
      " [ 0.44220765 -0.89672343]]\n",
      "\n",
      "First 5 y labels:\n",
      "[1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(f\"First 5 X features:\\n{X[:5]}\")\n",
    "print(f\"\\nFirst 5 y labels:\\n{y[:5]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATakj2bVWBou"
   },
   "source": [
    "Looks like there's two `X` values per one `y` value. \n",
    "\n",
    "Let's keep following the data explorer's motto of *visualize, visualize, visualize* and put them into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "id": "XAAqx_8sHC73",
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      "text/plain": [
       "         X1        X2  label\n",
       "0  0.754246  0.231481      1\n",
       "1 -0.756159  0.153259      1\n",
       "2 -0.815392  0.173282      1\n",
       "3 -0.393731  0.692883      1\n",
       "4  0.442208 -0.896723      0\n",
       "5 -0.479646  0.676435      1\n",
       "6 -0.013648  0.803349      1\n",
       "7  0.771513  0.147760      1\n",
       "8 -0.169322 -0.793456      1\n",
       "9 -0.121486  1.021509      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make DataFrame of circle data\n",
    "import pandas as pd\n",
    "circles = pd.DataFrame({\"X1\": X[:, 0],\n",
    "    \"X2\": X[:, 1],\n",
    "    \"label\": y\n",
    "})\n",
    "circles.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FK2T7GpYW2BE"
   },
   "source": [
    "It looks like each pair of `X` features (`X1` and `X2`) has a label (`y`) value of either 0 or 1.\n",
    "\n",
    "This tells us that our problem is **binary classification** since there's only two options (0 or 1).\n",
    "\n",
    "How many values of each class is there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cV8sVR9tHC74",
    "outputId": "dee5739f-1695-4e71-bb0b-de270f08b621"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    500\n",
       "0    500\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check different labels\n",
    "circles.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ytQ5rm9eXa65"
   },
   "source": [
    "500 each, nice and balanced.\n",
    "\n",
    "Let's plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "ANkSmESCHC75",
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   "outputs": [
    {
     "data": {
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exfq/j3ldNxgk5vxxG7ExFh+9qgZVVVFl2etluW7Usxz+aaaXgNaFhxLTvjmnVm0MOEBONBowVYulONXbwC2ZTaiKUhoNrMoynX98h5grWpK/9xBhDWoTWqdmmePn7thH4ZF0olo3IaRmYkBrutzQvHQuEXJ3HWBhp+GlnjG2E5msvvkJ2o5/mgb33uzVXlVVVt/8hKfuuEQgq04XOZt3407lWPa8crGNvG37fN6L7daOlq+PZW7jQWW+NARRRGc2YYqPwRQfQ1i9WuV+3vOJ0yWjk8QKeahUhpMnfbtn6nUiWVnF51XgCyXqtXNp/tKDpM1YhKuwGLUk2EyymGg7/hnqjbqBzDWb2Tfhf5xa/jeOnDx3G19/a1FEZzFhTT/pc/4zthPlrGRwq296DHSSW8Vmc5DQswM9Zn7hVYTGkZPHssGjyd2+H1EnITucJN98FR2/eUPz9w8CTeBfRGx/6WMvN0i52MrWpz/AVC2enE07sSRVo9aIwRgiwig8lIb9dLb/AYM43dlP5/i8nrV2C4e+nYpSzg5QNOhJvuXqgOcDt3D8beoO9u7NpEnjOG66sQXx8ZVTASxdfojxH63m+IkCwkKN3HF7G+68rU3Agv/kyUKOHM2ldq0IqiWEBT1/h/Y1OJaeh8vl+btXVBVZVliy9CB1kqOoW/ffM26GJicxeOtMdr35JSeX/42lZiLNnhlDtX5dAIjv2o74EvVP5upNLBt4j5cBGsBSuzq2k6dLXxqBoMoKyAquEpXhiUVrmBLRjriubWn27L0kDugGwNo7nyFn824Uh5Mzo6f+/ieRrRrT+NE7KvHpLy80lc5FxPTq3bAd95FUThSQTEbkYhuSxYyo19Fvxc8YoiOY03CgX9e5KkEUqXPbUA7/+Ifv2wY9iCKt3niMJo8HHmB9ICWLUaOn43DKOJ0Ker2EwSDy/cTrqF8vJqAx8vNtzFtwgKNpubRoVg1LiI5nnluEzf7Py8lk0nHHbW24954OZY7ldMq8OG4Jy1cexmCQcDhk2rapTnSUiWPp+bRvW4ObR7Qkppwd+omTBdx062SKihzIsvu7ZzRKJMSHcupUEZJOQHaptG6dyPh3B2EyXVh7MlVVWdTtZrI37y41QAuShC48BNXh8vkiqCiSxUSb95+mzq1DmRbbyad7b2i9WgxNCc7D6FJH0+FfIizodCNZ67cH1Da8cV2G7JnHjFo9saad8N9QENyGuSB2ZWcT27k19e+9iY0Pvur1ZRf0epo9O4b6o4d7eI4Ewt33zmDLVu90S6EhBtq0TmT4Dc3p2qW23/4pB7MYNWY6LpeKzebCYtbjkhUcDu/PabHoWbZoFHqd7xKAsqzw+FPzWLU61e98Br2IxWLg1//d6HPnX1TkICu7mMRqYZzOKuab7zby98Z04mIshIQa2LAx3WNtBoPE9dc05aknuuNwyAgiftf3b+MqKmb7yxM4/OMfKE4X0R2ac3r15vOSRkMyGxm8cw6zGwz0GXdgjI/m+pNrq3zeixlN4F8ipM9Zxl8jHvMMg/eTbl0yGRm0bSZzWwxBdfhXt+ijwnHmFbq/TIKAqNe51TMBPBe6sBAGrJ5EWINk/mw1lKLUjFL9rGQxUX1QT7pPnVDOKN6oqkqHLl+687j4wWTScdstrbh/jHfhtD9m7eaNt1eU7qDLw2iUmD3jH4Op0yWzYGEKS5YeRFEU0jPyOXTYf176M0iSwOBBjXjlxT6l1/buz+ThsXPIyrKWtnng3o7cdUfb0jZdenztceo4g9mso3GjOLZtP4EgQJdOtXjp+V7ExIQE9Ln+LRZ0Hk7Wum3nbfzqw/qSMWuJz+e8zh3X0PmHd87b3BcjZQl8zdpxEVFjSG/affQc+shwJIsJ0WREF+5fp5236wCqs4ydu+AOgy/dOakqgiQS162tO3WCTiKqXTME/TlqBVEkrls7rt6/gMgWjZBMRgaun0KjsXcQUieJ8Cb1aP32k3T9bXxAn8vhkJHPcv0TBAGjsezdrM3m4sf/bSUry/NUMWvOXt55f1XAwh7cO+fICLeR8PCRHG6+dTJvvL2cFauOsGr10YCEPYAsq8ybv5+J324gN89GZmYRt981rVTYn2nz6RfrWLDwAMXFTqZO3+lT2ANYrS62bD2OoqjIssqq1alcefVPLFqS4r5vc/Ln/P38PGkbu/ecCvjzVjVFh729jqqS43OX+3UsaPDAyPM696XGhaUg1CiX+qOHU/eu67BmnMIQHcH+z35h5yufeh6nBYHQBrXd+vOyduoqXnpR2e7EUjOREUvd5Yn3ffI/tm73TGUsSCLtJryAuVpc6TVDZDht3n6SNm8/GfBn2b7jBG+8vYKDh7KRJIF6daMxm3XUrhVJQnwoR1LLFrR6ncjWbcfp26de6bUvvv4buz1w9ZQkCSQlhbNsxSHmLzzAqr+OeBlUg8HlUvj2h838PmUnffvUw+XyVkOoKrz57nJ043VYrYGnnQD3C+P5lxaRk2Plsy/XI8sqTqeMJIl07VyLt98YgCR57uMKCx0sXprCxs0ZHDyYjcul0LNHMreNbE1EhMnPTIET1bopxxes8n0ziMyh/vBnBBbNRozRkZUa+3JDU+lcJOTuOsCuN74ge8seIps1oNnz9xHdpil5ew8yv/31pZGo4PafHrhuMttfmkDa1Pl+x5QsZp9ZEqPbN+fKDdOQHQ6mxXbyTtolCNS4ujc9Z35R7rpdLoUVqw6zdl0aMTEWhg1pTPXq4RxNy+PmW3/HavORGC1AGWGx6Pno/cE0axpPekY+CfGh9Or/bVDy5cxckiSgqqq/9DRBI0kC0VFmMk9XnRGzPAwGiaef7M61w5oC7tPKzl0neff9VdjsLo9TjygKhIYY+PXnG6leLbxS82Zv2smiHrd4ZEyVLGYaj72DwsPHOLXibwzx0RjCQslavy2o3EplYalVnWFHlv7rrrUXOpof/kVO1obtLO59O4rVjqooFOw7TMa8FfSc/RXrRz3nlZpYcTpx5BWQNm2B3zGbPX8fe97/zuu6aNAT38P9rFiPnfSdV0d1J94qD4dDZswDf3AgJQur1YVeJ/K/n7fyzpsD+GtNKg4/6qZABLYgQEiIntlz9nLfQ7MQBfdFk0mH1Rp4Fa0zcwWjAgoEWVYpKgoul31lcThkxn+8mjatE3nqmQWkZ+Rj8/FCBVAUlfwCO9fe8Asvv9AXvV6kWrUwwkIN1EyK8DollEV0u+b0XfoTW59+j+zNu7FUj6fZiw9Q55ahXm1tp7KY3XAgzvzC0l++oNf59+33hSAgmox0+v6tMoW94nJxYtFqrMczie3ShojG9fy2vVzQdvgXAQu73sTpNVu8rock18Celeu1Axf0OsLq1yZ/z0Gf40lmE8OLtrLlqXc58OWk0tOBIIrowkO4audcLDUScBYWMT2us0+3zrhu7ei/6tcy1z156g4+mrDWS0cdFmqgXr1otm4rw3vIDyEhehQFYqLNJCSEsGlz8IXTRAEknYjTWUXbeT/UrBlB5qkivzr684EgQFiYkcJCR5lG73P7CIKAoqhIknvn/8Jzvejb+/wIyLzdKfx970tkrtmCqJOoNWIwCX07sfHBV1GcLtQATwCi2UTjx+6g1WtjvYKvClJS3dXXCopAUVAVhZrXD6Tzj+9c8oFa2g7/IiZ//2GyNuzwea/oSDpSqLfft+p0kb/vsN8xa1zdC0EQaPPe/xHeqA57PvgOR3YeCX060frNx7HUSABAHxpC3VHXc+iH6ecc1000f/GBMte9aEkKH3y02qdQVVSVaglh6PWnghK6LVsk8PCDnQmx6MnJtfLgI3MC7ns2zZrFc/BQdlBzSxKoqns3GaggPXWykKFXN2b7jpPs23+6QmsNFlWFggJ7UGotVXV7RoH7ZJKXb+fFcUtIqhFBo4axVb7GiKb16b/qVxSXC0EUSwVw7eGDyVq/DUQRuaiYldc9hGLzf0pSrDb2ffQT+hALzZ67z+Peyuse8qrAljZ9EQm9O1Jv1A1V/pkuFi7tV91Fij0rh52vf870al35s+VQ/3lMRNGdFvfcyxZTmcfjVm88Drh3dfVHD+fqvfO5/tRauv32IaF1PXOZtPvoOeqPGeH2CirJk9Jx4uulEZBnyMjI50BKlltnv/IwL7+y1K9AVRW4alBDjMbA9xuCAI8/2pXWLavxw0+beejRigl7UYTeveoiCr4ffZ3OfV0nCeh1Im1aJdKhfQ3uv7cjSxfcxTdfDiNQlbHdITN77j6aNonj+Wd6XnBBVGXhcMj8Mun8uVoCiDqdx25bZzaR0KsjCT06cPrvHWUn7CtBLray+/1vPa4VHk6jMOWo13dALrZy4PNJVbP4i5SL5wm8TDi1cgPLrxoTWCUpRUGUJDCbUErylIgmI6G1qqMLtfjUs8f37EBYff8BS+ci6vW0+/A5Wr/zJM78QozRkR5f0mPpeYwaPYPTJe6RogiRkeYy1RiWED1dOtfi1Zf78vhT8wJah0EvERZm5I9Ze1i89FCFHT8EQSAhLpQJH17FI4/Pde9sVXC6FO4e1Q6HzcWu3ado0CCWm4a3ILGaZxDVwsW+1WT+sNlczJ23n/vHXMGatUdZuz4NWXZHDkuSQEFB1er5DQYJZ1muuAGiKCoZx/M9rjmdMq++sYyFi1NwuRT0ehGXS8FiMXDt0KbcNKIF3/2wieUrj2Ax6xl+Q3NuGt4iKHsAQPbmXex5d2LAOn1nbgGOgkLSpi4ga/129FFh+Hsru6z/QSnOCwhN4F8AOAsK2f7ixxz+ZRaOrLzg3NhEgWr9OmPPzMZVWEyt4YNo/OgdFB5KY1GPW1DsDhSHE9FoQDIbueKrVyu0RslgQDqnapGiqNx062SKi51nXYPs7LLro4aGGBAEgclTyzf8ls4viTgdMpN+3x6wSsUXsqzyw/82M+l/I1g87y7W/Z2G1erkig5JREWay+2/cFFK0C8bg17ixMlCPnh3ELv3nGLL1uPExljo2aMOA676gcJC/0JfkgSfBmVBAKNBwmaX0enOCF49cbEhHEvPC8gIXZY3lE5y6/SXLj9Ej27J6HQit981lX0H/qlf63C4T3CFhQ5+n7qd36fuQFFUXC6FLOCzL9eza/cp3nytf7lrOZsjv852F5oPEEvtGvzZfAiOrDxcRcUIBr3PU7FoMpJ88/lJQX2xoAn8f5HiYydIm76QorQTOLJyUGWF6kN6seuNL8nfe8gji2CgKHYHit3JwLWTPa5HtW7CVbvmsv/Tn8nZuoeYK1rS8MFbPHznA2XX7lPMnL0Hl0uhT6+6dOlcC1EUWLnqiIewD5Rj6fkUFNjZtj1wg6vRpKNevWgKg/B88VcFMD2jgHV/p9GlUy16dEsOeDzAHdnscy6BOslRHDqc7SVEHU6ZGjXcro9Nm8TTtEl86b1+ferxx6w9Psds1TKB3XsyfQrv6tXDef2VfsTHhrBqdSrHT+TTskU1fv1tO6lH/ccvCAJER5u55aZWWG0u/vfLVp+ePC5ZZeu2E+zes4jk2pE8+389PIT9uZwR/mdjs7lYuvwQacfyqJkUeN2BYDx2RKOesPq1OLV8fam/fqnR90zkuMOJLsRCSN0kGo29vBOtaQL/X+LQTzPYcO/LKC6XRyBJ6u9/uv2SK6ijEA16ots183kvpGYibd55qkLjgjvFwJj7Z7Jt+z/eNDNn7yG5dhQ/fns98xfuL6N32WzcnI5BL5XrQqnXCUg6iTdf648kifTuVYffJ5d/MjAYJNq3rcGadUe97tntLvYfyKJLp+BTNA++siGTp+70yHsjigKtWlbjuad7cttdUz0EqMmk48oBDfyeHu4Z1Z5FS1IoKvJ8cSYlhfPqy30ZPvJ3n/2cTplWLdz5iYbf0Lz0+kuvLPG7doNBom+fujz5WLfS9QjA19/694ZzOGQOpGTxwsuL/bYpC71OZM/eTGomReB0yWzechyX0514zmz2XQeh1ojBpEyc4jNGxAtB5NTyv30HZ6kqUW2aEFq3JtUGdCN55BAkQ8Wqil0qaEbbfwHbqSw23Pcyss3u9WAqdkelIhEVh5OMeSvIXLO5ssv04qlnFngIe3Av9UhqDq+8vpQly3zUSi2HM6rVl15ZQnE5UaZ6vcg9ozowY3mVbrEAACAASURBVPJIOnZIAuDh+ztjNvvep7gNrQJj7m7PyiX3MHBAfZ9tjUYdSdUrFmx07z0dqJMchcWiR5IELBY90VFmXn25L/XqRvPFp0Np2iSu1D3ytpGtee7pnn7Hq54Yxu8/j2DokEbExlqoVSuCZ57qzqxpt5JUI4LoKG8vLL1OpHfPuj7Hi4n2na3TZNLx17LRvPFK/1Jhr6oqP08qv7ShqrpPRRVBUVUSq4WxZetx+l/5A08+PY9nXlhEv0Hfl6aIOJe4zm2oN/pGJIsJRBFBr0MyGenw5SsYY6M8x7fZUV3+Nw1Z67eTOmkuG+57iY0PvYqzqBhVVTm9biv7JvxE2vSFyI5/N17iv0Tzw/8XSPlmCpvGvuERDRsQbgdpt1tLOX8myWKi/1+TiG7TtOILPYujaXlcc8MvVTJWRTCZdNxxaxvuHe2dtjg/38YTz8xny5YMFAVq1Yygc6ea1KgeTp/e9aie6Da0Wm1OhlzzM3m5NpSS51ySBGKiLcyecSt6fcWyTyqKyrq/09i37zTVq4fRu2ddDIbzk8ly0+Z0Hn5sLi6XgsulYDLpiAg38utPw4mK8j41zJ67l7feXel1yrjxumY89mhXj7ZFRQ669/nmvKwbQKdzq7m++/o6Bg750Uv9ZzRKTP3tZmr4eflmb95F2h+LkUxGao8YjD0rl6X97vSO/A4SY1wUriIrquyuvqULMdP/r0n/eVGeqkLzw/+vUVXUIA2NurAQmjx1D4kDuqK4XCzpdVuZhSVkq50d4z4JKN1BICxdFpw3SqCIouDT6FotIYTsHBs6nYgsK1w7rAn3jGrnc4zwcBMTP7+mJKmY4ldwm016fvjmOl55fWnJSUWgXdvqjHuxd4WF/ZnP0KVTrQqphIKlXdsaTJl0E1Om7STtWB7t2tZg6FWN/RY8HzK4EVlZxUz8bhOgoigqQ4c05uEHO3u1NZv1mEwSNlvlvXrArTKKjDCRm2dDVVVatajGiBtb8Mes3T7bK7LKnD/3+a1FEN22GdFt/1FXOrIDS2JXHvbMf4r5KHYHriIrf40Ye1kUWNcE/nnm1F8b2fvhD6Vuk4Eg6HQM2vKHx46jxx+fs370C6UFoL1QVXK3+y5DWBGcPpJ+VRa93q1B9CXwk2tHMfW3QZw4WUBCfCghIeXrWkVRQBTLFtw1kyL45strsdqcCAgXlS/8GWpUD2fsw10CaisIAnfe3paRN7Xi5KlCYqItWCy+deWiKHD3ne357Ev/BeyDwemU6devHreNbM3EbzYwZ95+9u4/jc3m8siGWtrepZBfEHgO/ai2zdBZTF47fCnETEynVj5rLgeEopC38wDWE5kVcmq4mNB0+OeR3J37WTbwbr8pDnwhGA00fuIuD2Ev2x0c+GISjpx8t17TDxFNqy4UPj09r8rGArdwMZv0iD78o00mHQP6N8Bi0VO3TnRAwj5YzCb9RSnsK4rBIFEzKcKvsD/DqDvbct/o9qUBZ5VGhXnz9zN3/n4cDpnCQgcul+LTTGU26+heRhGbcxElie4zPkMXFoIUYkHQ6ZAsZqoP6kHfhd8T26WtX//7cpftcpE2YzFKGfaASwFNh19FOPML2T7uE1J/nQMCJN9yNUVpJzg2dYHvBGQ+kELMxHZsRa+5XyOZjKXXtz77Afs+/rHMikKSxUTfpT8R27FV6bXiYidr1x/F5VLo3LEm4eGBp8LtPeA78vIqH6QiCG4jaeeONXns0S5s3pLBW++uwumUURQVs1lHg/oxTPzimkqpWTQqh6qqJaUkRV5/azkzZvp2FS0LnSQiiASUskKnE6mZFM7H468iqUbgLptAaWJA++kc4nt1JPaKlgAosszBiZPZ8tS77uprFRBt+ogwes//hthOrYPvfIGgVbw6zyiyzPx215G35+BZPsDuWp/BFHTu+tt4ao/wDgyZGtsRR5Yf/aUgEN6oDu0mvEBi/3+McqvXpPJ/zy1AFARU3GX6nvu/nlw9pHFAa2nb8fOA1w0wckRLps/c7WUsfOj+joy8qZVH2wMpWUz/Yxc5OVZ696xLnz51L5jyfRqwdt1RnnpmQbleVGcTbNp7QXAH00mSwONju3Ljdc3L7xQgssPBkf/NJOXryeTtTsFVGFyKan1EKNceX43OXPlaAf8FmtH2PHN83koKUlI9s/yp/gs3+EP04yMs+9P/CwI3Fm5Gb/F0xcvPt/HUswu8AmrefHcFrVsnegXBHEvP5423l7Nr9ynCw4yMvKkl1RNDyTheWOZ6BQEiI0y8/EJvenSvQ9Om8Uz4bC2ZmUVEhJsYfU97brqxhVe/BvVjePrJHmWOrfHf0fGKmiQnR5JyMNtnDeAziKLb9bSgIPDMnGdQVUo8j+CDD1fTs1sy8fH+q7cFg2QwUO/uG6l3942cXr+NhZ2GB7c2WSFj7nJq3XClz/tFqenk7T1EeMNkQuvU9NnmQkUT+FVA9uZdwbtcnoskEtG8gc9b1fp1IX3Oci9jbUyHFl7CHmDZisM+VZl2u8zd986gc8da1KsXhdGgY87cvezak1naprDQwYcT1tC8WTzZ2TafOXFMJh1PPtaVPr3rlZYGBHdQ0uArG+J0ydqO/SJGFAUmfnENE7/byOy5ez1KNJ7BbHa7zf74vy2VSnUBIAoCK1Yd4cbrq26Xf4aYK1oGffxQFQVnnnfcgexwsOaWJ0mfsxzJaECxO6g2oCvdfv/IQwV7IaMZbauA0DpJ3nVfSxAk0Z2AvRyqDehGeINkn/faffgchsiw0odKNBrQhYX4zYtjs7n8fglPny5m9ty9fDRhLe+8v8pD2J9BllV278nk0Yc7UzMpojQcQK9zH8G7dK7F0CGNPYT92WjC/uLHbNbzyIOdWfTnXTz9ZHeMRneyN0FwC/ue3euwfccJnxXLgkaosK21/KEFd64pfykxfKEqKgl9vd1Yd7w0gYy5y1Fsdpx5Bcg2OycWrmbr0+9X4YrPL5rArwJq3nClu36sDwSdrnzjkSQR3jDZ7+3QujUZsm8+zV64nxpD+9DkyVEM2TOPqNZNfLbv0rny/uGKohIRbmLmtFv4c+btREdbEEsSaq1bn8ZNt06mIAiXOo2LlxE3tuDnH27k1pGtGH59cz58fzBvvNrPbzWtYFEUlZ496pB5uoinn1tA5x5f0aXn14x7bSn5+ZV3HGj/yYvoI8LcRQ3KQTKbqDvqekzxMV73Dnz1u5fjhGyzk/LtFC5UW+i5aEbbKuL0+m0s7HKTbx/5AJAsJm7I3oBkrBqXxAmfruXnSVsrXJD7zLG+TetEHnp0Nuv+PuZxatDrRYYOaczzz/SqkvVqXHxMnb6T8R+vqbDgPxOd/MSjXcgvsPPlxA0eieIEAWrVimTabzcjBnBKLgvriUy2PPUeqZNmo54bEyBJhNRMxBgbSf6+w6hOF4osE9+zA11/HY8pLpqCg0eZ3WCAb9WQIHCza/cFU0lLM9qeZ45Onc+Gh1/7R9gLAqJBV5IULcBBFBVHTl6VBH4cSMliyvSdFRb2AFGRJlq3qobTKbN+wzEvFZHTqbBgYYom8C9jhg5pwuy5+0g56K5ZfEZVbjCIJCVFkJFRgCgKuFwKtWtFkJNjxelS6dCuBvXqRhEWZqRbl9o88fR8Dh7K9hpfVSE1NZf2nb/AaJSIiw2hb5+63Hpza2JifOcM8oe5Whydf3ybnC27KNifiuJ0O1gIooghOpw27z/F2tuf9qjsdnLxWqYndqPRY3eQ8tmv+Psyx3RshavYSuqkueTu3E9Uq8bUHjEYXUhwa/w30Hb4leT4otWsGHqfdyk2UUQ06FF81IP1hSEqgusy17oLmlQCVVUZev3PpKdXLNnVGebNvp2E+FCcTpkuPb/2maI3JETPqqWjKzWPxsWN0yWzbPlhVq9JJTrawrVDm1CrViTgzkqacbyA2BgLYWH/GDUzMvJLHQucToXPvlyPK4jIbkGA665tytNP9Ag6YMyencvGh14lbdpCVFkmoV8XrvhiHGtueZLTa8tPJOeBJKIzm+j623jWj3oeV1ExriIrulALurAQrvx7KpakasGNWQVoO/zzyI6XJ/iuu6koAQt7yWKi1dtPVFrYgzvpWWZmcH7H5xIVZSahxEVOr3enGd6wKd1jl6/TifTvW79S82hc/Oh1EgP61WdAP+9nwWjUUSfZM7vlr79t45PP15VqRlwupUIunTNn7cFk1PPE2K7ldzh7TdGRdP11fEmlM7VUDVOUmhHUOODW9/dZ+hM7x03AXlLfAsBVWIzLamPjo6/TY9qnQY97PrkwlE4XMQUHvfOtl4dkNhLTsRWGqAgiWzWmy8/v02DMiCpZjztnScVPbaIInTomeVx78fneREeZS8P0LRY91RPDePQhb08GDQ1/HD2ayyefr8Nul3E43P8q6tLpcqlMnb6zwuUcBUHw0LnHdGwZ9BhykZWVw+7n+IK/vO0CskLG3BUVWtv5pEp2+IIgXAl8DEjAN6qqvn3O/TuB94D0kkufqqp6/vKyVjGKLHP4xxmkfPU7itNJ8q3DaPjASCSTkahWTTixaLXvjqIAPh5oVVXpNfcrjDFRPjpVjjrJUZhMehyO8k8XidVCycm1Ybe7UFV3OluzSc/9Y67waFc9MYxZ029l6fJDHE3LpUG9GHr0SNbcLzWCYunyQwGVXgwUl0uhqNhJZETln8OWrz7K8fl/+Q9y9IWqYs/O87u/EoKs5ftvUGmBLwiCBHwG9AeOARsEQZilquq5OVF/V1X1ocrO91+wZuTjpM9dURpclbfnEAe++o1aN1xJVOvGnFy2zjuqVgBLUjVsp7I8VD6S2USt4YMqJex96UZlWSE310Z4uJHHH+3CuNeWlTvO2Ee6EB8fyg8/bSY9vYC2rRO58442VEsI82prMukYfGXDCq9ZQ0NRVJ/ui/5KUZaHLKusXpPKVYMaVXptkc0bMnD9ZFYMvZ+iI+nldyhBtTt8+vgLBj21b7rw6udW2mgrCEJnYJyqqgNLfn4WQFXVt85qcyfQPhiBf6EYbXO27mFh15s8rPdeiCKgeu/mRQFRr0cfHoojJw/RoKfe6OG0efepoEutKYqKIMBPP2/l6283ICDgkhUG9KtHo0ZxfP3NBux22R3uHmrkdFZxmcGF/frW5d03fYeOa2icDw4fyWHk7ZOx2z03R0aDxOh72vPb5O2cPh1cxHp5RVSCRVVVMv/axL5PfyZtyvygq9EJOgnJZCS0bk36rfgZQ2TVrCuoNZxno20NIO2sn48BHX20u14QhB7AfuAxVVXTfLS54Dj116bys136u6+o7iLjDgfXnVqLPjw0aMPsjp0neevdFezbfxqdTkJRFI9j8dx5+5nzp2dtWZvN22ir0wnUrhVFYmIod93eljatqwe1Dg2NylInOYrRo9oz8buNuFwKgiAgSQIP3NeR20a25vZb2nBF1y+DGlORVf6ct49rr2mGIiuVzscjCALx3dsT160dv81YhOoMLsZAMhvpMeNzEnp3vGD88s/m3/LSmQ1MUlXVLgjCvcCPQJ9zGwmCMAYYA1Cr1oVRbsycEFPpP5zicFF48Cgx7b0TiZXFkdQc7n1wZmlgiy8DVaAbEFlW+e3n4UgXoF5R4/Jh1J3t6N2rLkuXH0IA+vWpV+rGqdOJtGiewI6dJwMez+lS+H3KTr79fjMI7mIxb77Wn0YNYyu1TkEQSOjTiRML/gqqn2xzUM1HWoYLhar49qcDZ6eMS+If4ywAqqpmqap6xor4DeCzdp2qql+rqtpeVdX2cXEXRuUZfVRE2eqcAJCtNvRhIUH3++nnrRX2QjgXVYXlKw9XyVgaGpWhTnIUd9/ZjlF3tisV9md46fnehISUXbTlXHJyrTicbq+fw0dyGH3/H1WS9qPNO08FlYMH8CjJeCFSFQJ/A9BAEIQ6giAYgJuAWWc3EAQh8awfhwLBV1f4j9j23PjKDyIIFcqmt//A6Sr1alix8kiVjaWhcT6oVzeamVNvwWgMTPUpSYLXKdfhkJm34ECl1xLVqjG1hg8KrLEgIFnMtPvoOQDyDxxh09g3WDHsfvZ98j+cBWWnGv+3qLRKR1VVlyAIDwELcLtlfqeq6i5BEF4FNqqqOgt4RBCEoYALyAburOy8/xa5O/aX36gcBL0OfXjwusWmTeLZt79qhL4oQkTExZHCVePyJjragsViwG4v34Cr+nB7djhk9h84XSVraTv+WY7PW4Uzv5zaEKJAvXtuILZTazIWrGLVdQ+hOFyoLhcnFq9h7wffc+WmaefFFTsYqkShq6rqn6qqNlRVtZ6qqm+UXHupRNijquqzqqo2U1W1laqqvVVV3VsV854PZIeDorTjyCVRsuYE76x5HogiLV59lIjmDXz63Qp6HdUHdMMQFVwZN4BePZIrnWv8DHq9jmFX+86uqaFxoWExB7YX9ff1SM/Ir5p1VE+g1dtPIJQTc6LKCilf/kbm+q2su/MZ5GIbakl9XLnYhvX4KXa/M7FK1lQZNAteCaqqsvudiUyL7cScxlcyNaYjW5/7gKbP3YdkMfvtlzigKy1efICrdszh+qy/afjI7YgmI/qIMCSzidhOrej80ztlzm2zudiwMZ1tO06UCvgFiw7w2FPzgvUK80KnEzEadTzzVHfq1yvn5aWhcYEwdEjjShVWz8qqXHqRs7FUjy9TBpxBcThZ1PVmn+VIFYeTtOkLq2xNFUXLpVPCwW+msOPVz5CL/zlG7n7ra6QQM3Hd23JyxQbUc3LmCDodrd5+svRnQ0QY7T9+nuYv3E/u9n1YalYjvGGdMuddtCSFca8tQxQFVFXFYtHTtXMtZs6u/CFIkkRG3tSS0aPaExJSNWmXNTT+DW6/tQ1r1qaxfeeJCm16IvwU56kI1fp1QZUDdJ6QFZRz0yyUoKuA40ZVc1nv8G2nstg34Se2j/uEbS985CHszyAXWclctQljVIRXAQVBJ3Fqxd9efUxx0VTr27lcYZ96NJeXXlmK1eqkqMhBcbGT06eLq0TYgzv6dsYfuzEEaADT0LhQMBp1fDfxWj56fzCdrkgiNEjPnajI8nfkgaILsdD11w+QzCZEkxEq4NosWcw0euT2KltTRbls0yOfWLyGFcMeAFVBtjmCjqg7Q1jDZK7et6BCfT/+dC2/VKJISSBYLHq+/eraSvsla2j8V6iqyuKlh3jh5UU4nYHlYBAEaNUqgTYtq7Pu72OYzXpuvK4Z/frWq3AsivVEJkenzMdVUISzoIi9479317woD0mi/j030Oz5+zm1fD36yHASB3YLOto+ULT0yOegOJ2suvFRnzv6YHEWFFW4b26u9bwKe3Dv8s/ORa6hcTFxJDWHh8bOISfHFlQZQVWFrVtPsnXrP0Fc27YfZ+78fXz0/lUVqqBlrhZHo4dvK/05JLkGG+57udx+ok7EWVDM7IYDEEQRVQXJqKfvkh//db/9y1Klc3rdtvLTJQSAoNORNKxvhft37VIbsynwd65BL/nVTQoCSOc8xJIk0LB+LNUTvZOhaWhc6Miywn0PzuL48QKsVmfp5kioYNFzWXbXY96w8Vil15a9aSebH3+r/IaAYneS+utsFJsDudiGYrXhzC1gUbeRyK6qqQscKJelwJet/7hMVRTRaMAUH02LlyuWAHTajF288dZybPby16HTiTx0f0fW/XUvyxaOolnTOCTJ84k3GnUMG9oYg0EiNNSA2aSjbp0o3n9XS5CmcXGyectxioocXtpWQYB69aKpVi00aMHvcqn8tTq10mvb9c7EKonA3/7CR5VeSzBcdiqdPR9+z7bnPgy4GpVPJJGGj9xG8+fvxxAR/O55wcIDfPDR6oCLP+t0okc2wAkfDuH5lxazaXM6kiRiseh58bne9OyezIP3d2LP3kxioy00aBCDUJGtkIbGBUB+vs1nagNFgUOHcioco1IVHjwF+49UegyAo7/9SZuzPP3ON5eVwM9YsIrtL3xcOWEPhNWvTZt3nqqwMP3qmw0BC3tw++mPe30ZDerHULduNFGRZj6fcDU5OVYKixzUqB5eqpOMijTTpdOFkXhOQ6MytGqV6NdIW5mAxNq1I8tvVA5x3dqRu3M/+HHBDBTZ4aM86nnkslLp7P3gu0obaiWLiV5zvqrUzvnkqeDzarhcMtP/8KwpExVlpmZSRIUMUBoaFzqxMRbuuK01piDsXIHw7AuLmDp9V6XGaPp/96ALsfi9H9mmiduFsyxEkepX9qjUOoLlshL41hOVz6+RfMvVhNWvXeH+TpdMUo3g0yzIskp2TuW9ijQ0LibuH9OR996+kh7datO8WbyX7aoiKIrKe+P/YtLv2ykuDsCt0gchtapz5cZpGKJ9FzjJ3boXxW5H8FP/QjToMUZH0OKVhys0f0W5rAR+9UE9EX34vooGfbm5Ms6Q0LtThef/fcoO+g74ntSj3qHX5aHTCaxZd5Q+A7/j1TeWkZ1ddaHjGhoXMl071+KjD67ip+9uoGGDWMQqsEs5nTIfTlhNvyu/Z+68fRUaI7xBMrLVj0pGVUEFVQBTYjx17ryWzj+/S/Jtw4hq14yQuknINjsLOt7Ijlc+RXFW7MUTLJeVwG/y5CgMMRGIxn+Evmg2YamV6F2T1heiQN6uiqVdXbzkIB9/upbCIgcOR/A57l0ulfx8O7m5Nub8uY9b7pyK1frvPCQaGhcKb78xgOgYMxaLHoOhchHkLpeKze7i9bdWcPhITtD9ZbsDyVyO2sYlY8/MpuUrj5DYvxui0UDOlj0U7D2Mq7AY2/FMdr8zkdU3P1HBTxEcl5XR1hQXzeDts9j30Y9kzFuFJSkBR24BWeu3BTaAoqLYKyZkv/42OEOtAPgzS7lcCvl5Nv6cv5/rr72wCy5oaFQlNZMimDvzNlavPsrJU4WEhxv56eetHDqUjUtWEITgC6K7XDIzZ+9h7MNdSDuWxxdf/c2mLRnERFu464429O9b36uPPTuXBR2H4yws/6StulzMbjgQyWxyp1k+Z4Gy1UbGn8spSEmtlLo4EC4rgQ9gio2m6TNjCElOInvTTjL+XBHY7r6EhAqUL8s8XUTaseDUOOX5IFhtLrZtP6EJfI3LDr1OolfPf/JUDRrY0O3CicDE7zYydfpOZFnF5QpM8suy+/SckZHPLXdMobjYiaKoZGYW8fKrS0lPz+fO29t69Nn23HiKU9MDrnmr2B0odv8eOaJeT+72fZrAr2oKjxxjYcfhuIqKcRUFZwQVDHr0EYEXMrHbXbz0yhKWrTgc8MMXKEaDRHJy5d3LNDQuBcLD3b71T4ztyn2jO5ByMIv/e24hmZnlpz4RBIGePerw7Q+bsFqdHi6fNpuLr7/dyIjhLTCb/kngdnTKfJQgC5yXhSLLhNY7/+7Ul6QOX3E6/ebd2HD/OOync4IW9uB+MELr1iy/YQnjP17Nyr+OVLmwB3cw1rAhWkETDQ1wJ1hbsOgA9z44k4fGzuFAShY/fnttoL1p1iSOzVuP+6wuJ0kCaUfzPK75876pCIJOR1SrxkS1alxlY/rjktrhZyxYxaaHX6Mg5Si6MAuNHr2DFi8/hFjyx1EVhROL1lQoj45kNlHzhoGYEwLLOulyKcyasxe7vWqKkIui+4UjCALJyVG8+lIfYmL8+wFraFxOvPrmchYuPIC1xE62b/9p5i84QPXEMDKOF5TZV6+XSDmYTX6e71QJTqdCbKznd63O7cPY/+kvZappAiXpuv50/Pq1So8TCJeMwD+9biurrnuoNL+FK7+IvR98hzOvkPYfP+9uJAggChCkDJZCLDR86BZavfZowH0cDrlSO3tJEqhbJ5rTp4to3jyBB+/rSM2kCJxOufT4qqGhAYcOZzN/wX6PzZXN5mLPvkzuvac9H32yrsz+UZEmnnh6Hlart4rGYJDo2rkW0dGeAr/FuIc5tXIj+XsO4grAcAtgTqqGPTPbveEUBGI7tabLL+8RkpQYUP+q4JIR+DvGfeKVzEgutnHw699p8OBIDnz2CycWrsYQFY49Kw/8VbARhH9y40siCf260PV/72GKiy53Daqqsmv3KfYfyKJmUgTVq4eTlpZXbr9z0elEunWtxfh3B3vdM5uDKwShoXGps3lLhs/rVquL48cLeeTBTkz4zL/QT0wM45QfXX/P7smMe7GP13V9aAgD10/h1PL1LL9qDLLVT7qWkih4yWTCkZ2LYncgGg2IBj3tP3mBkKRETq3cwKbH3yJv5wFM8TE0e/4+6o8ZcV7yYF0yAj9v7yGf1wVJKjXSllrUy/g9htZJoujocfdbWFY4vWIDcxoNZMC6yWVWsLLanDw8dg579mSiAqIoEBFu9Hh/BIIoCiTXjuTF53oH3klD4zImMsJcUtTEcxOn14tER1sYNrQJn36xzqe7ZlSkiZSD2T6/o0ajxOOPdvW7yRIEgYTenahz2zAO/TDDZzEUUadDF2rBkVdQmnfnjMfO2tuewlK7BhlzlpW65RWnHWfz42/jyMmn2TNjgvo9BMIlY7SNatXYZ5Js2W7HWVjk6T7lRwCLRgOyw+lOnVzydMg2O47cAjY9+kaZ83/59QZ27jqF1ebCZnNRXOwk83QxtWtFBpXCVZIE3ntrYJWWaNPQuJTp3q22z4LnkiRiMklcfe3PfnfLObk2Cgt96+FVFcIjyi8e1PrtJwmpk4Rk8Va1Kg4njuw8n0nWcrfvJ2P2Mi95JBdb2fXml+clsdolI/BbjHsYyez5C5csZvSRYeDLz/6cB0AQRXQWM9b0E95tVZWTS8vWA86eu9crgtblUjiSmhvUDt9o1JVrZNLQ0PgHo1HHV58NIyEhFItZT0iInvBwIw/e15EvvtpAcbHTp/dNWUiSQId2NTDoy/fGMURFMGjbTKoP6e32rqgCVFnBVgW5v87lkhH40W2a0mfRd8R0bIVoNGCukUDrtx4nqrUf18VzpHBk68YM3DAVQef7+CaVk/nO6awabxyHQ6Z+vZgq3GevagAAIABJREFUGUtD43KhUcNY/px5GxO/vIbPPr6aRfPuZNfuU9gdFfOVl2WVzVsyuGv09HIj5A/+OIM/avQg/Y/FwYf5loEpvurlwCWjwweI69KWgesme1wLb1KP02u2lpsWOX/vIZYNugfV5SN1gkFPjWF9sZ487dcts0f3ZBYuSgl6J3EuPbsnExcXUqkxNDQuRwRBoEnjOAoLHfw2eQdr1h0N6nR9LlabiwMpWfz6+zZG3dHOZ5uMeSvY+MC4Sle/OpcGD95S7iazIlwyO3x/JPbvSpv3/w9dqAVdmH9BKhfbKDyQ6lu/73RybMYiZtbuzbLBo3Hk5ns1GftwF6KjzEHVqPXFnn2ZQRVr1tDQ+IecXCs3jvyNL778m/z8yhU6ArDbZeb86T+b5s7XPg9e2JdXv0IUqHld/+DGDHTq8zLqBUbD+0dyfeY6+q/8BVNiXPADqOAqLEaxOzi5ZC1/DR/r1SQuNoQZU27h0Yc7V6ogSVZWMfv2V73uTkPjcuDb7zaRlVUcUK3oQJHK0MsXpfp2CZXMJqLaNkM0+FARl1OtSzQYMMacn7Qpl4XAB0AQMMZF+3SdCgbF4eTUqo0UpR33umex6Bk0sGGlBL4oin69BjQ0NMpm+UrfeasEAZ+ePOWh04l07lTT76k7pmNLn96BokFHr3lfE9/zCnc69kBd9USBiMZ1CW+QHPRaAxr+vIx6AaGqKttfnsC0mCuY3WAAjqzgi4+ci2jQYzuR6fNeaKihUikPZFmhWdP4CvfX0LicCQnxLnAE7gybISHBBy26XApTp+/iljumkOOj4lzLVx9FZzF7CHTJYqbl649hjo+lz8LvaPvRs37z5p8pvCRIEpLFRHjjuvSc/WXQ6wyUS17g73nvG/a8/x2uIiuyNQhdm1Dyx/BxnFNdLiKaeubItttdTJm2k3sfnEVcrMWnO1dSjTBMRt86flEQMJl0PPlYNy2aVkOjgtw8ooWXHU2SBFq0SGBAv/rodMGfvm02FykHs3j2xUVe9yKbN6T/mknUuKoXxrhoIls3ofNP79DooVtL27jyi1D8eAvVHjGYjj+8RXyvK4hq3YRGY+/EGBsV9BoD5ZLy0vHF7ncmBlW4XBcWgmQx0ubt/yO2a2sWdb4JR16hOxiLkrf3q494FDB2OGRGjZnB4SM5pS5cBoNEzaQICgrs5OXbUFVIzyggIT6U3DwbLpdCRISJjlfUYNeuUxQVOWnUKJbGjQJLzqahoeHNsKubsHt3JrPm7kWvF1EUleqJ4bz1+gD0OpF169NIO+btdFEeLpfKlq0Z5ORavYIio1o29rsrd+TkobhkRL2E7PIU+rpQC5LFzMYHXnGnZlBVcrbtJeWLSQxY89t58dIR/p+9sw6P4uz68D0zqzEghCRAcJfg7lagUEpbvAIUWurCW/dSd1q+UqEu1LCixa24uxMsgbgnayPfHwspYXeT3SRQCnNfV0sy88hsdvfMM+c553eu1oiQNm3aaNu2bSvVGKqi8Juhsd/tg6pXpsuMKVRs0xTh/Mo+PyGJfW98TuKy9VijK9Ho6fHE3Ny7UL/5Cw/xzvtrPcSXJEnwGqbZsH4EX342GA2NO8fOJDU1H7tdRhQETGaJF57tzsAbG5TgFevo6AAkJuVw8FAKkZWCadwosiDTdsWq47z4yvISqdhaLAZm/DqSqlW8Fy6/lIMffcfu5z9CU1WPQimi2URI7RjyTiSg2AtHE0lBVlpNfo56E0YEfI0AgiBs1zStjbdz17RLR5QkQur6V1RAslpo+/kkIto1KzD2WQeOkbHrIE1fuJ+bjy3jhnW/eBh7gNVrTnhV2vMVk3/oSCp5+U5+n7GPlOS8gqcCVdOw22XeeW9tiere6ujouImOCqVn99o0aRxVSFbB4VDO6+4EjtksER3lXwGk5LVb2f3ix6gOp4exF0xGt/ruGxO9RvEo+TbOzFpSomssjmva4AO0nvy8h+TCpYQ1rkP3eZ9TdUB3AJxZOSzrejuL2w5hwx1PMq9eXzaNew7Vh8JmeLg14Mic43EZrFodh8ObYRfgyFE9NFNHp6xp1zamxLLleXkuXnx1hV95MkemTkf1sWcoiCLVRwzEXCnce20OQcAcrodlloiqN/Wkx8IvkYJ9i5GVa1yX6D6dCn7fPP550rbsQcm348rKQbU7OPX7Xxye8pPX/kNva4rxkk3a4qKwGtSrSDkfuvaKrBEaWvb+Ox2d652IikFMGN8GSwkSJGVZZdnyY2zeEl9sW3tKus9zqsPJ0vbDWNF7jPvmcamx0DSSVm/mzBzPTeLScs0bfIConh2KrEWrXZQIIeflkzB/lUe8vpJv48j/eTf4DepH8OxTXbFYDAQHmwgKMhIaYsZXFTRBgL+WHGXUyGYeHzxRFKhevRw1quv1anV0LgfjxrbmsymDSpQvo6oaP/2yq9h2YQ1r+z6paaBpaC4Z1SkjGg0erh17Yiob7niSc0vXBXyNRXFdGPy8M+ewJ6X5PN/gkYtCqIoI3XRl5/o8N3hQI1YsvpsP3u3PZ1MGoaH5rLGiaTDtm6107FCNMXe1RJKEgpu8ySjy4P3tin5BOjo6xZJwNptHJy6gXecv6NR9Gq+/taogqbFF88oMG9IEsynw2rRnzxavZhvqZ+1rzeU6H/rtRdrdZmfPSx8HenlFcl0Y/KSVm4qsHrPrhY+wJ7tvCOaKFXzKL0R29brxXYDVaqR92xhqVC9frMKeLGtkpNsICzVjNEgFIk92h8KzLyzj4CHviV06OjpFE3cinRdfWcYtQ6ezfsNpZFnFbpdZsOgwDzwyr8AH/9jDnWjXLgazWQrIxVO3TvHV7yq0alykG/liVLsD1Yf2fc6x035flz+UicEXBKG/IAiHBUE4JgjCs17OmwVB+P38+c2CINQsi3n9xVgutMiNlrQNu1jScQSqLCMIAtZo7wbf6WOFr6oaS5Yd5aHH5vPw4wvYtOUMpmJWDk6nzNp1J/nsi80euh92u8zULzYX86p0dHQuZeeuc9w5diZ/LTmKomiFtBBdLpW4Exns2euueWGxGPjkw4HM+HUk773dj4YNIjCbi1/xr9twinETZrNrt6e8ygWienYgrEEtBFPpUp0uTfAsLaU2+IIgSMBU4EagMTBKEIRLg9/HAxmaptUFJgPvlnbeQKjSvyuioeg/vP1cMmcXrQEgffs+r21S1nrmBWiaxrMvLuW1N1excdMZNmw8zWtvrCIkpOhNV02Dt99bS77Nu7bP4cP6Cl9HJ1DefGc1drvsWxZZ04g7kVHoUEzVcnTpVINvp93GmLtaFRtw4XAo7NqdyIOPzGf3Hu9GXxAEei//Hslc8uALyWqh+ZsTS9zfG2Wxwm8HHNM0LU7TNCfwGzD4kjaDgR/O/zwT6C1cjgq9PpAsZrrMmVpkG8XuIPt8XVxfNwfRi1zCnr2JrFt/qlAcvs0uk5Tk299/gfN7N16pXNm/5A4dHR03DofMyVPFaGUJUKumd+kCi8XA/fe2ZdSIZn5JMNgdMlM+9V0JL3Xzbu8nRLHoMD5JpHyLRnSf/wWR3doWex2BUBYGvypw5qLf488f89pG0zQZyAKuaFknc7kQ8CZVegENgmvFAFB95ACPXXPRZKT6yIEe3bZsTcBRhlKs4P7g3Xdv2b7ROjrXOgaDiNHo26QZjSK1alagebPoQscVReX7n3Zw46Af6NrrK06dziQ6KhSrtXh3zNHjvoNB8k6dRfNWXlVVqdihudcauACSyUiHb98iunfHYucPlKtq01YQhAmCIGwTBGFbSkoZuzQ0DaGYMKzQ81m5rSc/T7km9dxaF1YLhpAgyjWpR+vJz3v2CTNjKqWfDtzhmEajSMWKVl58rgedO/qXIayjo+NGkkQGDWzoNfJGkgRu7Fefzz+92SOA4/W3VzPt620kJeeRl+diw8bTJJzNLjbwAigy8za8dRPfK3lVQwrxrqqr2BysG/YYWQePFzt/oJSFeFoCcHEMUsz5Y97axAuCYADKAR63Rk3TpgHTwK2lUwbXVkDFds0Q8F7QCgBJJLh6FQBM5cPov302yWu3kn3wOOUa16VS1zZeI3369anLlE83lvr6Rg2PZfy4NoSFmkulp6+jcz3zxOOdSUvLZ/3G05hMEk6nwo396vHicz28SiqkpOSxeMnRQlIm/sqLmc0S90/wHUJdsU0sEZ1bkrR8g4fhydh1kGpD+3Fq+nyvfXOPn2Zx61tp/Nx9xL70kH8X5AelFk87b8CPAL1xG/atwO2apu2/qM1DQKymafcLgjASuE3TtOFFjVsW4mmXsvO5Dzj4zldez0X2aE+fVT+WaNzNW+N55PEFJU7ZBhh8U0NeealXifvr6Oj8Q2JSDgkJ2dSsUcFnfYrUtDw++Gg9y1Yc88vIXyyGGBJi4smJnbn5pkZF9snYe5jFrW/z0NMB3Kv/YiaWrBb6bZ1J+Sb1ir/AgmF9i6eVeoWvaZosCMLDwBJAAr7VNG2/IAivAds0TZsHfAP8JAjCMSAdGFnaeUtC7MsPc/KHudgSUwr9oQ1hIbT7+vUSj9u+bQxvTOrNy5NWllj0bMmyYzz3TPdiwzl1dHSKJzoqlOioUK/n5i88xJSpm0hLy/d7PKvVwM2DGtGiWTQtm1cmIiLYryfxrP1Hvfvxwa9HCdXl4syspQEZ/KIoEx++pmmLNE2rr2laHU3T3jx/7OXzxh5N0+yapg3TNK2upmntNE2LK4t5A8VgtdB/+yyqDe2HaDYhmowIRgOK3cHCRgNY2mWU+2ZQAnr3rEPrllVKfG2qppGTU/qiyzo6Or5ZsvQob7+3NiBjD+5cm9tHNKPfDfWIjAzxy9jbU9LZMuFl/31EvijDgMaratP2SmCtHEnXPz6h35YZaKpbz0JzutBcMqnrd7C82x1+qeFdiiSJDL2tSYmvK8hqpHz5olU9dXR0SsfULzb7tRl7MRazgW5dalItplxA/eK+n11QOKmkiEYj1Yf1K9UYF3NNV7xypGVwcvp88s6co1KnVlQd1LMgxn7XMx94fTNyjp5iRlhrzJUq0OCx0TR45K4Cffyi2L4jwWsJNH8wGkUefbhDiXW6dXR0fKMoKi6XisViINGP/BhwR82ZTBKhoWaG3daEMaNbBjxv7rHT7kpWgWI0IAoigiQQ++ojlGtYJ/AxfHDNGvy0bXtZ0WsMmqyg2OwcC/mNkLo1aDPlBVRFJXnddp995dw85Nw8dj8/mezDJ2j32avFzvfJp5tK7L8XgLg4d/bfseNppKTk0bBBJSpU8E+LQ0dHxxOXrDDl003MmrMfp1OhapUwKoZbSUrOK7avqmpYLAYWzb2rxAuxiM6tODl9PnJeYO4jg9WCqUIYde4dTsP/jS3R3L64JkscaprG/Hp9yT3uKTwkGA3ed8x9IFpMDD65CmuU91qziqKyfuNpnnp2MS5XyaN0jEaRmjXKcyY+G4Mk4nQpjBoey6MPdyxS+E1HR8c7r76+kiXLjhYqZ+ir7Kg3gqxGvpl2Kw3ql6zOtGJ3sDB2EPmnzxbIrUtWM6bw8tgSkortLwVZierZnu7zvwjIBlx3JQ7zTiVgO+v9DxqIsQeQzGayDxzzei4zy87QUb/x/EvLSmXswV0O8Xicuwh6bp4Tp1Phj5n7WLLU+9w6Ojq+ycyys3jpUY/atf4aewC7w8UXX21h5eo4FCXw77dkMdN68nMYLiRYCQIVWjam44/vIfrIsi10rfk2kldvJnXDjoDn9sU1afBFg6HUG+MXcOXkkX3kpNdSZB9OXsfZhGzy870LoAWCqmqoauGLttllfv61+GILOjo6hUlKyi1SZsEfVBXWrD3Ji68sDzjPJu/MOU7PWsK6EY/jTM9yH9Q0MnYe5Pi3M6l8Q2dEYxFSL+dR7E6SvYg2lpRr0uAHxUQT1qBm2YQzqSo7n3yX1Tfd51HTdsWqOFylSLbyh+xsPVRTRydQqlYJK1Ui5MXY7TI7dp1j5erio8ltiSks7TyKBfX7sX7ERJT8wgWVFJudU78u5OzitQgGyV2/NjqC4JqXyo+5ES1mLJHF6+/7yzVp8AG6/PEJ5krhGEKDEYyl25uWc/NJWbuNhLkrCh2/3PsfRoNIty41L+scOjrXIiEhJkYMiy1R7VpvOJ0Kfy05Umy71Tfe666HbXeg+Sp5p6poDheKzQ6ahjMtk3wfPn1BEqg+7MbSXHohrlmDH1a/FrecXk2Hb9+i5ftPU+WmHojWkse5y3n5nJ6xuNCxHt1qIUmFnyIE4XzFslIiigLlylsYN7ZV6QfT0bkOGXZbE5o0jsRkFJEkgbp1wkulU5WX570q1QUy9hxyu38DjL3XXLLPvcUef32DMcy3QFugXLNhmQCS2UT1of0BqDasP3Or9Sj5YKKIIbSwJseTE7uwZ28iWdkO8vNdBFmNmM0S+TaXx2ZRoISGmJjxy0jKldOTsXR0AuXAwWQmPDAXu0Mu2Bs7cTIDo0HEUcLw6fbtYoo8b09MRTRKlO6b/w+CQaJCs/plNJqba9rgX8yWe15078JciiAgGCVEo9EdOiUKoKge+heSxUSd8cMKHatYMYg5M+5g1eo4jh1Pp1atCvTuWZvnXlzKpi3xBRl9JqOIM4AoHkGA2KZRJCbloigq4eHexZ90dHS88+Y7azyqySmKhuLLzVIMkiQwanjzIttUaNUYxeElgMMPkTRvBNeoijEkOOB+RXHdGPzktVu9n9A0Ygb1JvqGThz97BeyD8UhmE0osg1BkpCsZlRZJvbVR4ho7/mGm0wS/frW4+Lk5/fe7s/vM/Yy+88DOJ0K/frWZdny45yJz/LrWkVRYMu2BHbePweXS6Vr5xq89mpvrJbid/V1dK53MjJtHDwUuCaWwSDSvl0M4RWsLF56FEVREUUBURR46/W+BAUV/f2zRITT6Im7OfTxDyh5NsBdOOlCDH5AiCJtP3818H7FcE0mXnljVmRHHCnpXs/VfWAUyau3kH04Di4KjRQMBip1aUXn3yb7TLzyl/c/+pvfZ+zzCL28gNvvL1C+nKUgDv8CJpNE7561efO1G0p1DTo61zo2u4uRd/zOmfjsgPqZzQZuubkhEx/tjMkkceRoKus3nMZqNdCnd10ifEgsX4qmaZyZvZRDH32PMz2Tck3qkbBoDarNXnzni5FERuTuQrIEXhP3uku88ka9B2/3HqYpihz/bhbZB48XMvYAmiyTsm4H5gjvNTD9Zcu2eGb/ecCrsbdYDFSLKceShWP5e9U9REQEeUg0OJ0KK1bGFbtppKNzvbN4yVFSUwOTMhAEWLfqHp55sluBPHn9ehHcdUcLGjWKJDExx+/EK0EQqD6kH33X/8pNB/+ixqiBCCWQZhAE4bJEAV43Lp0mz99H6qadJC7fCBe/eaqKZvdtSDVZZtWN94Ki4MzIIubWG2jw6GhM5bxrbXvjrXfW+NzE1TSNTh2rERRkxGoxkpZu89pOECEn10FwsMnveXV0rjc2b43HFqAaZmio2UMvZ/PWeJ59fgmyrKLhXph9+N6NNI+N9uifunk3u579gIwdB7DGRBP7ykNUH9qfuO9mcfjT6QXunUCI6NgSQymiCn1x3azwJZOJXou/pfqQfgHH5SctW0/Syk1k7DzI/re+ZHHr23Dl+Ke6l5/vIj7B9+Olw6EwZ+5BHpu4EIBWLSp7fRAJCjJSKaJsN3B0dK41KkeHYjD4b9YMBoE7RxXem0tPz2fik4vIynaQl+8iP99FerqNhx6d7/GUnbZ1Dyt6jSZ59RZc2blkHzjGprufY3H7YWx77E0ydx0s0YZt+2/eDLiPP1w3Bh9AVRTOzF4asJ5OoTHsDmznkjn+7Sy/2ptMUrGxv06nwp59SRw6nILJJHn9fIy5q6Uun6yjUwxDbm0ckMHv0rkm48a2LnRs8dKjaN722jQ8sm13PT/ZM5s2307Gtn2eK3s/M/8je3ckrF5Nv9oGynVlQU78+KfvcmMBoOTbObtgtV9tDQaRjh2Kjt8Ft9F/7H8LWbLsqMc5QYCDh1IDvUwdneuOmKrlmPz+jURUDMJqMRRr/J1OxWNBlp5h8xqr73IpZGYWNu6Zuw76f3GahiD5LmEqGA0YQoNpO+VF/8cMkOvK4B+ZOr1sBhLAmZmF3UfUz6WMGBrrV7uU1Hxk2XNloWlw+HDJSi/q6FxvtG9XjcULxjD9x2HMn30no+9o4bPtps1nmDu/sNFu1zYGq9XT7SsZRNq0Lqx5E1zD/7KmotlElZt7IQVZMYaFYAgNxhIdQcyQflTs0Jz6D97OwL3zKde4rt9jBsp1s2kL4MoILFTLJxpk7j3C/Lo30Ofv6VRo1rDI5iEhZqxWAzZbyVxJggB161QsUV8dnesRURSoWaMCmqZRu7bvKDtV1XjnvbV061qTCuXdBYfatq5KqxZV2LHzbMEGsNVioHu3WjRqWKlQ/9hJj7Ju+GMebh2vCBDZuRVNnrkXOTcfQ0gQFdvG+lVRr6y4rlb4VQf1RDQVnTxhCA6ix7JvsVaNQgqyIphNSFaLR2iV6nDhys5l87gXip23aZMoQkoRXSMI7mSSmbP3lYkUs47O9cJrb67i7ffWFtlGMois3/BPsSRBEJj8wQCeeaobrVtVoV2bqrz0fE/emNTHo2/VgT1o++VrmItTtBRFVKfMziffZWmH4Wwa/zwIAjuf+YBN457jzOylqKWsf+sP103iFYA9OY2/Wt6CMz0Lxe5FdlgUqTakL13/+ARN08g5dgpDkJWgqlH8Ua4VcrZnaTTBIDE0fQvG0KIFjg4fSeX+h+fhcinYbK4S6fULAoRXsPLLT8P1iB0dnWI4eSqDUXf9UayuVVCQkfsntKVh/UrUrVuR8iXQr3Jm5TAror33PUJR8MjxuYBgMKDJMobgIMLbNKXXsm/90skvCj3x6jyWyIoM3LeAJi8/SGiDmu434vzjlGQxY6oQRsv3ngLcd3ljaDCIAsnrtoEvAy0ICIbiPWMN6kewZMEY3pzUhyG3NilRcQZNg7R0G1M+3RhwXx2d640NG0/7lTBls7mY+vlm/vf0X/S/6Qc++b8NASc9mcqFElK7mveTPow9UKCsKeflk7ZtLyd/WRDQvIFyXfnwAQyhwaSu34ktPtn9RkgCSCLVRwyg5ftPY6kUTvK67awb9ij2pDS3lRUEryFVgsFAlX5d/U6QMJkkunerRfdutWjaJJJ3P1iLzRZ41NCqNScC7qOjcz2QlpaP3SGjKCpTP9/sNQjiUkRRwOFQCp4E/pi5jzp1wrlpQNF7c5fSbtrrrB5wL4rd6RZqNEgQQFSgkmfj5K8LqD3m1oDmDYTraoUPcHL6fJJWb/6nkryigqKSMH8VpgphpG3fz/Jut2NPTP0nYULTCittigKGkCBC69Wg/bdvleg6br6pEUsWjCU6OsRDU19HRycwUlLyGDdhDgMG/8Swkb8xfNTvxWbcCrgTry6tc2uzy/z4c+ClRSt1bkXVm3q614aiGJCxv4Ah+PIq4153Bj/u+zleU51Vl4u0LXvYcOcTvt03BY01Wrz/NAP3L8RSqeTlx0JCzAwf0jTgfuXCzBw+osfl61w/2O0yX3+7jb4Dv6dLj6+4ffQfbNueALjlSSY8OJe9+xJxuRTsDhmnq2hjK4oCk17uicFHXHxScm7Abp3dL0wmYcEqNEX1LsVeDFKwlXr3jQi4XyBcdwbfl5CR6nSReeAYOYf8c5ckrdqEUMqaufn5Lr78eqvHCqM4EpNyufue2Xz3Q9lVs9fRuVpRFJUJD/zJ59O2kJqaT77NxaHDqdz30FwG3Pwjvft9S3x8VoDfI40O7asTHOI9ei4vz8VP03dhO5fMulET+SOkBTPKt2brQ5O8yqpoqsrRqb/4F57pgwaP3Enlvl1K3N8frjuDX2f8UKRgq8dx1eFk+2P+61dk7z8GgCMtgz0vf8LidkNZe+tDJP/tf2TR/322sUSVsTQN7A6ZqZ9vZtac/Ze9tq6Ozr/Jho2nOXw01SOyTdPci5/MLAdKERuj3tA0WL32BC8+18Or9Imqanw3bSOL2w7lzMwlyHk2XFm5HP9mJit6jSn4zil2B/HzVhD3wxzkQCWQL8IQFkKLt58scX+/57nsM1xl1BgxgIT5q4ifuwLFZivkvlHz7W7fmx+PY8F1qmFPTeev5oNxpGWiOpykb93LuaXraf3JC9S9Z1iR/Q8fSWXWnAOlei2qpvHuB2vZuz+JV1/sVaqxdHSuVnbuPocrgIpx/iCKAnaHQveuNQkNNZOV5WmsaycdwpGRXSjUUnU4yT4UR8rf2xDNJlb1H+/W3VFL5sYBEExGao4aWOLXEgjX3QpfEEU6//Ih3eZ+5j3DTVURzMZiQy3DWzfl4Aff4kjNQHX8o6Cn5NvYMfEt73H+50lKyuXBR+chy6X/EMuyxuLFRzmi+/R1rlEiI0NKVHw8LMzM0Fsbez0nSQJdO9cAoGaN8l7bROQkoeZ72e+TFTJ2HWT1gHtxZeYgZ+ci5wamwX8BQ0gQoXWq0+Kdy7+6h+vQ4F+gfJO6Po26NTKC/ltnYizvW/M+on1zzi5a47N8WdZ+TxE0cG8wPfz4Ag8RpoupVzecoCCjv+J6OF0KDz02n7/XnSw45nDInD6TpWfm6vzn6d+3XkFhkkBwOGRMJgM3DahfEAknAGazxF23t6RGdbehv//etljMhW2BxWKgZudYr+5f0WjAlpRa5KLOXzr+9B4D9s7HVD6s1GP5w3Xn0rmAtXIkIbVjyD5YWO5UNJuoMXIA1pgoqg7qzcmf/vToaywfSnSfTj4rYcl5Ngw+3sCjR9M4ey7bZ6at0Sjy2ZSbqVDByrr1J3n+5eV+Ge20dBtPP7+U11/txalTWXz7w3ZAQFFUbrm5EU9O7BKQbKyOztVC+XIWvpw6mPsenIvd4b/8gMOhMHPOfhbPH83gmxuzZOlRJEnkxn71aHZRIZP27arx2iu9+ODj9aReHjTTAAAgAElEQVSl5WM2GRg5PJa7RzVmYf0FKDZHgbtGMBpA0zjw3tclCru8GMEgETO4T6mDPwLhujX4AJ1+/oAVPUejumQUmx1DSBDWmGjMkRX5s2o3T6MsCFiiI+iz6kdESaJC8wYkr9rsObAgkHP4BGF1qnucSs+wIfkQSxIEeOTBjlQ8Xz+zdauqAUUeOBwyb727Frtdxn5RDPK8BYewWgw89kgnv8fS0bmaiG0axQP3tWPK1I0BfSdMRokz8dm0blmF1i19K1v26V2X3r3qYLPJmM1SQe2Jvht/Z8uEl0heuxUEEUtURewpGaU29gDBNateUWMP17FLByC8VRNujltO87f/R/1H76LtF5OwVq7ErqffR3W60Fz/rKxFs4mGT9zNrQl/E9agNgCC6P0xU5Akcg57D+9s3KgSLi8xwpIkMGp4M+68/Z/qO8HBJp54vBMWi//35cxMeyFjD+4Y5j9m7fO7LqeOztXEug2n6D/oByZP2RBwCLPTpRAdXbTO1QUEQSAoyFio0FBo3Rr0XvkjI+x7uS1pPY6UDDRHGdSWFgQaPDa69OMEyHVt8AHMFSvQ8LExtPnkRRypGaRu3Om1JJnqcJK4dH3BHVnTNCSrGdHsGccrmY2Uj63vdb6wMAv3jGuD9SIjbjJJREWFcP+Edu65VI1jx9M4cTKD225pTOeOPjQ6AsDpVD1uBDo6Vzubt5zhsf8tJDnZU7iwOCRJoFuXmmUiNKjYHWx54NVCARqlQTBI1Bk3tEzGCoTr2qVzKXHfzkItoqC5YJBQZZk9L3/Cwfe+QVM8V+qiyUhI7WpE9ergc5zxd7emYYMIfvltDxmZNnp2r8XI4c0ICTGxc9c5nn1hCbl5TjTNreSXl+vfh0wQ3BV/zsRneZyLiAgiKKh0Knw6Olea195cVSJlWXAvykYO96/4UHGsHjiB1E27vZ8UBQRJCqh0qjmiAoYgzw3hy41u8C+mqE+WJBLRsSUr+99D8grvapWC0UCtMbfQ8v1nii1q0LlTDTp3qlHoWHp6Pg8/Pr9QoZRAVuURFYO4Z1xr3n5vLQ6HXPByLGYDT/2vyxX3F+rolJakEqzsL6CqMH/hIVoV4bv3h4zdh0jftg/NW0TeeVXdHou/YVXfccg5ha9XNBk9IvkEo4GYWzy19a8EusG/iFpjb2PPSx97T49WVI5/9YfPMEwAY0gQ7ae9UeL5Fy0+ghpgxuDFZGTaeevdNcQ2jSI4yMShI6lUiynHhPFtPEqz6ehcjeTkOpg5az/rN54iKjIEURBQixW38k16umccfcDXdOSEz1q0QdUq02fNT4TUjGFY5jYOfvQdx7+egSCJ1HvwDsLq12TtLQ+hulxoLhnJasEUXo7YVx4u9XWVhFIZfEEQwoHfgZrASWC4pmkZXtopwN7zv57WNO3m0sx7uaj/0B0kzFtJ+vZ9XhMpijL2AEox54sjJTU/IKkFg0EslLwlyyqyDNu2n6Vn91osmnuXvqrX+c+QlWXn9tF/uIuIOxQEoeiHbn9o27b0C52wxnW9FjYRJImYW3oTUjPG/bso0vjJ8TR+cnyhdgP3zufIZ9PJOXKSyB7tqTN+KKZyvnN8Liel3bR9FlihaVo9YMX5371h0zStxfn/rkpjDyCZTfRe9SPd5kyl6i29Ec2B+bwrdW5Vqvlbt6qC1Vr0nJIkYLEY+PjDAUx6uafP5KxVa07QrffXdOw2jQcensfRY2mlujYdncvN9F93k5ZuK1j0lNbYCwLcMsh7pm0glG9Sj8hubRAt5kLHNUXh2Be/se/tL4vsH1K7Gq0+eJbu876g0f/u/teMPZTe4A8Gfjj/8w/ALaUc719HEASi+3Si5sibEE2B1aFNXruVxJUbSVy+gb9a38rvwc1Z0LA/p2cu9qt/547VqVfXt9yyJAnccnNjfp8+AqdDZtIbq4v8UuTluXA4ZDZvjefue2cTn+C5maujc7Ww5u+TOJ2BxbeLIlgskkcmrtEoMvrOloT4UMMsDlVROPXHItbc8iDrRk6k6s29MYeX82zndLHn+Y849PH3JZrnSlOqmraCIGRqmlb+/M8CkHHh90vaycAuQAbe0TTNM331Ei5HTdtAcOXmMSe6yz+FUvzFS5UbKchCm6mvUGfsbcV2dzhkhoz8lbNnczzOWSwGVi4Zh8kk0XfA96Rn+O+flCSBWwc35vlnuvvdR0fnSjLhwbkFGvf+0qtHLR5/tBNHj6Xx4eT1nEvMITjYxOg7WjBubOsSafBoqsqam+8nafVWFD+//4LRwI3bZ1M+tkHA85U1pappKwjCckEQ9nn5b/DF7TT3ncPX3aPG+Qu4HfhYEIQ6PuaaIAjCNkEQtqWkpBR3aZcVY0gw3eZ9hiEkCENoMIaQYESLmfA2sUULq3nx9Sn5drY9NImsQ8eLnddsNvDKC728anuMGBaLxWIgJSWPvPzA4oEVRWPf/qSA+ujoXEnuGNWcYoLbCiEIMPGxzsRULUfP7rVZ8OddbFx7H2uWj+eecW1KZOwBzi1dR/Ia/409gCYrHPn81xLNdyUp9s+raVofTdOaevlvLpAkCEJlgPP/JvsYI+H8v3HAaqClj3bTNE1ro2lam0qVKpXwJZUd0b06clvSBjr+8C7tpr3GLWdW03/rTOpOGB7wWEq+ncWtbuX07KXFtm3bpipvvt6HypVDEUWB4GAjo+9swSMPumP7Q0JMaAEmzQoC1KrlXftHR+dqoFOHagQH+e+CsVgMHklVJpNU4kAFOd9G3umzxM9bGbj6paZhP/fvLlL9obRhmfOAMcA75/+de2kDQRAqAPmapjkEQYgAOgPvlXLeK4YhyEq1W28odCx9x/4SjaXYHGwe9zxVb+qBVMz+QM/utenZvTZOp4LRKBb6EAcHm+jRvSZLlxf/xHABTYNt2xLo2M29wdS8WWVsNhdn4rOoUa08D9zfjnZtYnz01VAUTRdf0ylTbHYXu3adQzKI1K0TzgMPzyfHzyRDi8XAvePalEhF81JUl4ttj71J3HezEUQBVVFBEt31rv1FEqk6qGepr+VyU1qD/w7whyAI44FTwHAAQRDaAPdrmnYP0Aj4UhAEFfcTxTuappWu8se/TM7RUyXuq2kaGTsOENGhBc7MbOL/XI4zM4fovp0p37guAKosgyAgSp6bURe4fWRzlq+MCyhuPyX1n1XLlq3xBT9nZiby2P8W8d5bfenapWaha/3hp118/9MOsrMdVK0Syv8e70zP7rUDfNU6Ov+weWs8H0/ZEHBd5gthmmaTxOOPdmTYbYHXg74UTdNYM/hBzi1ZV+ICJgDGsBBq3j6o1NdzuSmVwdc0LQ3o7eX4NuCe8z9vAMomv/kqwVIpHGdaZon6qrIMaOx5+RP2vzPNHd97fuPcGhNNSK0YUjfsBEGg6qCetPtiEpbIih7jVK4cisEgBhzV4AuHQ+bDj9cXMvjTvtnGDz/tLMj2TTibwwsvLeej92+kQ/vS6/voXF9omsarr69k4V+HS2RbL8SXqJrG3+tOMXxIyc2KpmmkrN/O1gdeJWuf99oVgdBjwZdIl4RtXo3oz+gloNFT4z0KI0gWs1sruxg0WWZF77Hse/0zt/bGRVFStvhEUv7ehqYoaLJMwvxVLOsyCtWLZk+liGBat6qC0Vh2b+HpM1kFTwwuWeHHn3d5Km86ZKZ85l1aQkfHF6qqMfGpv5i/sGTG/mJcLpUtWxPIyCw+Si3vzDk23PkkMyu248/q3dn/zpcoLhdbJrzIihvuLtrYSxL+VCGKvqEzlTqVLgfnSqFLK5SA2ncPITfuDIc+/A7RZERxuqjYoTnJq7cU21dzyihO//RxNFnGlphK4tJ1VLnRM5zynTf68vTzS9i56xwul1LqRBVRhPZdvsBgEGnfNsannPLhw6kkJ+cSGemf7KzO9cHGTaf5+rvtnD2XQ93a4fTuXYeunWpQsWIQa/8+yabNZ8psLoNBICfHSYXyvgXIHOmZLG59G470TFBUnOlZ7H7uI3a/8LHbkHtZSBVCU4vM/hKMBuo9cDutPni6pC/jiqMb/BIgCALN35hI46fvJftwHEEx0cTPW+mXwQ8U1eki+8hJrwY/NNTM5/93M8nJuUyZuollK46Vqtize+WloSgKa9f53qfQNPjm++0897Qe06/jZv7CQ7z93tqCJ8KkpFzWbzyNwSBy6+BGZGbay8z9CGAyGahSueiM1WPTfkfOzfPcfPX3EaOY/bGwhrVp88kL/o11laAb/FJgDAuhYttmAITWqwGiUOyHJFBEo4HyTb1r618gMjKE557uxtFjaSSczSY/30WQ1YggurNtLwcbNv6zWtu+I4Eff95FUnIu7dtV4647WhBxvmqXzrWPoqhM/mSDV2VXWVaZM/cglSqV7vMgigKqqiEI7lyV557uVmzUWMq6He7yhJcJR6qHbNhVj27wy4ioHu0Jiokm//S5MhtTNJkIrVuDqJ7ti20bHGzilx+HsW7DaQ4dTqFK5VCio0K476F5PvtYLQYsVgPZ2fZin24vJTzc/Sg9Z+4B3nl/bcGTxfHj6SxYeIjfpo8ok8ITOlcnmqaxZ28iy1fGIbsU8m2+FxayrHLuXG6R4zWLjaJHt1p8+fVWDwFBk0li0MAGHDiYQtUqYYy+swVNm0R5HUdxOjm7aC35Z85hLBfiNfM9YAQ8U0olkeg+/72SobrBLyMEUaTflpmsG/4YKeu2+17pC4JbI/tC5RxRAA0kq4WYW/qgOp2c/WstgihS8/abaPne08Vq619AkkS6d61J9641AfcmrCQJXsvChYWaeOqJrjRtEsXQkb/iO0na7du/+ClYFAVEUWD1mjjeendNofEVVSMzy86332/nmSe7+XXdOv893n1/LfMWHi5Ud6E0vDGpDzFVy9GubQyPPbEIm81VsF/6+it96NG9VrFj5MadYWmXUci5+ahOl/t7E8gTtyAgGDwLmRjLl0N1ONxPC5qGaDJiCLbS7LVHA3mJVwWl0tK5nPzbWjqlQc63kbnvKCt6jkbJ/yeSQLCYMAYHIefZ3NWyVI3wNk3pvfonDJcppGvUXX9w5GhqoS+lwSDy3lt96XE+nn78fbPZuSuxyHGMRgFVpZBxNxpEXLJ3f2hYmJllf43FaCh9YozO1cWevYnc//C8Mi2ZeevgRrz0vDtxSVFU9h1IxuVUaBYb7Xdy1ZL2w0jbtq/Q6kQ0m9BUtdhqVFKwlbCGdTCGBZO+dS9yng3JakGQRHot/RZBEjnw/jfkHjtNZPe2NHpyPEFVvT9l/NsUpaWjG/zLSMqGHWx9cBKZew4jWS0EVa9MXtyZQrr6osVMcK0YbPGJiCYjtcfeSrPXHsMQZMWZmY1oNGAILuz/tCWlcvLnedgSU4ju1YHK/br6fApISsrlwUfnk5iYgyAKyLLKmLta8MCEf9xE5xJzGDj4J5+vQxRh8KDGLPrrMI4ANt7atanK1CmDChWF1vlvoSgqp05nEhRkJDrKvUn6yf9t4Mfpu8pkZX+BmwbU57VXSl4FypaUytwaPb3WnDWEBntUoiqEINBlxifEDO6NIEkkr95M8tptWKIjqDH8RkwVPFUyr2aKMvi6S+cyUqlTKwbsmosqy6Ru3cvyzqM8wrxUu4Ocg/9IJByZOp2kVZtRHS6yj5xAQCC6b2c6fP82lohwklZtYvWg+0FRUewOjn3xG+Gtm9Jz6Tde5RqiokKY+dtIDh5KIT3dRpMmkR6hbJUign26fgBEUeTsueyAjD3A3v3JrNtwusDFpHN1oaoaf687yaLFR5AkkUEDG9ChfbUCGY/1G0/zyqQV2OwuFEWjXt2KvP9OPwxGEVH0/XkpCaOGNytVf01WfMbMyzl5iGaTzwLkQdUqU31Iv4Lfo3p2IKqn75rU/2X0pdcVIHPvEVb2HuNXRQfV7iRjxwGy9h9Fc8moLhfnlq5jZe+xKLLMuhETUfJsKHZ39IGcm0/a1j0c/3qmzzEFQaBxo0i6dK7hNW7ZYBAZNqRJEf1hz56iXT7esNlc/L3+ZMHvdrvMps1n2L4joaBSV1JSLkeOpuJylV3I3vWMpmns3ZfEzNn72bT5jE/pDU3TePGV5Tz/8jKWrTjO4qVHefLZxbz93lry812cPp3JU88uJj3Dhs0m43QqHDyUwv0PzaN/33p+6ypdsMEmk+/2XbtUp1GjyIBf68UEVY0ipGYR1a00DcHoWVxICrL+a+UG/w30Ff4VYM9LH6PaA5MzvhjNJZMbd4aTP/5ZYOgvRsm3c+LHOdR/8PYSz/HkxK4cO5bOth1nCx2XJIERw5oxc/a+gMcURQg/f4NZvuIYr7y+ClEU0FQVBAGr1Uhmpg2TSUIURZ55sis3DWiApmn8vf4Ui/46jCAI3DSwIZ06VCuxCqI3Es5ms2VrPGGhZjp3qoHF8t/+KqSm5fP9DzuYM+8ADoeCJAkYjRIVw4P4ZtqtHmGyu3YnsmbtCWwX+eFtNpmZs/cza85+wsODcF6SIKiqGvEJ2cyavZ97x7Vh2jfbioytNxhEBt/UgIhKIZQrZ6F8OQtvvbsGl1PBJauIosDDD7Rn9J1exXN9omkaaZt3k33kJOVj6xPe0l3VqtP0D1jcZoj3hZUo0mzSIxz97Bds8YmAgCE0iNhJj1Jn3JCA5v8v89/+lP9HSN9xoPT12hCwJab6HEf0snoJBFEUmPb5LRw4kMzsuftJTs6jVYsqjBgey/cX6ekEgqrCDX3qknA2m5cmrfAIt8vPd+9l2Gzusd96dw0xVUOZNecAK1fHFRxfu+4kA29sUCbFWzRN4+MpG/h95r6CaCNJFJg6ZZDPUL+rDZdLYfnK42zbnkB0VAhdOtfg4ccXkpVlL1jRq6qGy6XicGTz6msr+PSTwsJe6zecwu7w/p5qGqSleZcHVlWN32bsw2AQuXt0KyxWA4v+OszxOM+YdFlW2b7zHLP/+Gch0rtnbfbuS8JolGjSODJgzXpnVg4rb7ib7APHQQBN1ajYLpYeC6cR3qoJoQ1qkXMozvO6XTKJS9YTUiuG6N4dKd+0PjVGDsBauXRPFv81dIN/BQiqXsV/rWxJclvKSwy7bLNz+OMfkPM89UNEs4k69wau0e+Nxo0jady48Jegfr2KJSooLUkCZ89mc/Bwil9Zlna7zGdfbGHfgeRCNxibTWbBwsMMG9KUenU9heQCYcOmM8ycvd/jeh57YiFLF4696jeY8/NdjL1nFglns7HZZExGkWnfuIMbvLlvFEVjy7YEbHYXVss/iwKjSSzVGkSWVX6cvpNPPxlEvxvqMWTELx43dHAXJtc0reDpzGiUaNWySonn3frQJDJ3HyoU+JC2aTe7X5hM68nP0/iZe9n64KuolyZcKQpJK90aUMmrt4AosvO5D2n3xSS/KtFdK1zdn+5rhPAWjXyfFATMURURJBHRaKBy746YKpZzG/6L2qCpOFLSvVpdTVa8anHLefmcmbOM0zMX48zyLJnoLzVreFSt9IsLft64uHS/jcup05k4vKw8ZUVlw8bTpKfnc+BgMjk5JcugnPPngUJujAs4HQq7S7BPcaX5+ZddnD6TVfD043SpKIpW9AaqBor8z/lFfx3m+x93lvpaHA6FGTP3UTk6hKgo77pK2TkOvvxqq99jZh04xrGvZ5CwYBWqy23U03ceYNWAe5lVqQOnfplfyNgDKHYHh6f8xPKed3H081+LDcEEQFXRHE62PfAqeWfKLlnyakdf4V8B7ClpPs+JRgM95n+BMbwCK3uN5tzSdedPCEjBViSLGTknz+NDfjGapjK3Zk+iurej+ZsTKR/bgIRFa1g//HF3YhduIbZ2X71JrTsC1+x2uVSsFmOR2ZTecDoVZszeT3JK0VmWFxBFyMl1enfBCrB85XE+n7YFo1FCdikMHdKUiY92Csgt4NONgUZ6ej6qqpW4NF6gZGTamDl7P/v2JVG7dgWGD42lcnRhfZh9+5P47IvNHDmWRrWYciQm5QasSVO5SigJZ7OZv/AQhw+nsHdfMs4y2iTPzLIjCALvvtmPO8fO8LjxKIrGT7/sYtzY1kXG02uqysYxz3Bm1lIQQJAkDEFW2n7xKhvueKpQPotXVLVEWlaapnFm5hIaThwbcN//IrrBvwKUa1yXswvXeA0LqzthBOFtYplRoQ1y1kWGUdVQ8mxUH9afE9/PKXoCVUPOziNh/ioS5q8iuEYVbImpHvNtufdFKnVuSUhN75WtfFGnTjgGowj+10wH3A8jGzae9ru9quJzr8DpUjl0OBlFocDgzZqzH1VRiTuRwbG4dGpUL88D97WjdREugxv71WPnzrMeq/z8fJkXXllO0LtrePiBDgy51XfUUlmQcDabO8b8QX6+jCyrbNx8mhkz9zPt88E0Ph+xsmv3OR58dH7B3yQ93eaPWq8HZ89mc9fdM1FVtdTSxJfSu6c7A7ZB/QjCwsxkZNg92mgapGfkF8TxeyPuhzmcmbMMxfZPfzk3n/WjnkD1EqhQVmiqiuoseUDFfw3dpXMFqHf/KMRLtPIFSaJc03q0nvIi8XOXFzb2F5G8ZiuGkMCEp/JOnfV6c9EUhVO/LgxoLACjQWLSS72wWAxIktviWCwGqlYNY8xdLTCbr0w27aV6P3a7zK9/7GXz1njS0vLZsfMsjzy+gEWLj/DZF5u594E/eevdNZw89c+GYr8b6tGsWTRWq/v9uNiAulwqWVkOPvx4PUuXH7usr+W1N1eRne0sCE+VZY18m4vX3lxFZpaNjEwbH32y3uMGWBK/u6JoyHLZG3uAXj3rFPxcp3a41zaCIBBeofBnWFNVDk35kbm1ejGjfGt2THwb5dL9KU27rMYeQDRIVL3Zo4bTNYu+wr8CBFWNovfKH9g8/gWyDsUhCAJVBnSn/ddvIAgCaZt3++zrOp80Qp6t1JE+qtOFK8c/98qldO9Wi19/Gs7M2ftJTMqhc8ca3NivHqqmsWVrAqdOZZJvcxWZwOULs1lCltUySeSx22VenrQcg0HC6VTYtfscCxYd5tWXetGkcSRVKocy9ZNBrN9wijV/n2D+wsMektJ2u8wXX22hb5+6uFwKBoPoERKqKCpbtsaTnJJH0yZRREWGYDCIfoV3qqrGtu0JXs8dOZpGr77flfwPcAUJCzMXyut4YEJ7Hnx0fqE9GIvFwJi7Wni4c7Y//hbHv5lZvKvGFxfeD3++Ez4iDqQgCw0njqVcozpeOl2b6NIKVxhnVo5bfMlqKTh2bsnfrOp/j9f2kT3a0e6LSWwc/TQZOw8CAiH1alCuSV23vzPAZZtoMlK+WQOavzmRyn27lOalFOCSFdasOcnmrWew2VysXnOyWH+/KApUqGAlOiqE8WNb8/GnGzh9OqtMrscXJpNEZKVg3nmzL40bRXLyVAa3Df/Va9uLbYTBIDL6zhY8MKEdkiRyLjGHe+7/k6wsO4qiFriYJEmkU8fqvPJizyILc+zZm8jYe2aX+esrS0wmqci9AovFwCMPdmDUiMIZsps2n+HDT9Zz4kQGFcpbuHtMK0aNaFbohmlLTGFuzV4+M1/9wg8pcsFgQLSYqDGsP+eWrkd1ughv1ZiQejUwWC3UGDGA8Nalr4t7taFr6VzlaJrGnMpdsCddUtRZELhx9zwqxLr18J0ZWSAImMqHATCnaldsZ5NLNKdktdD598nEDOpVqmu/FKdToe/A78nO9v0obrEYuOXmRjz9RNeCY8tXHOPlSSt9bqqWJUFBBn74dih3jZmB3UsooS8iKwXxzFPd+P7Hnew/kOw1DNJgEKlZozy/Tx/hNVHs9OlMxt4zi8ysy+uq8BdBcFePkmUNo1HCaJRwuRTuGNmc5JQclq+I85DUqFDewv0T2jH0tiZeX6OqKBz/egbHvvwd1emk5p030+DR0YhmE3tfnsKhyd9dVp16JBEUFcFoILx1E/qs+dmr7Mi1im7w/wM40jNZ2Xc8GTv2g6ZhqlieTtM/oEq/rl7b58cnMq/eDaXK4A2tV4NBR5aWuL8vDhxM5pGJC3E6ZBAEnE6F6KhgEpNyCQ01c+eo5oy+s6VHNMzipUf5v6kbOZfo3e1ktRoQRaFMirpcKvnsL2aTiCxrKEWsLoOsRj795CZaNK/M6TNZfDh5HZu3xmMxGxAEyCriZljWWK1G+t5Qh6VLj7mznDV3iOujD3Vg6G1N2bsvEYdToUXzaE6ezCQtLZ8mjSMJDw9CllW++X47v8/YS16ek2ax0Tw1sQv160cUOeffQx/l7F9rC9w1ktVMucZ1ierVgSNTfym5G6cESEEWWn7wLPUfGHXF5vy30Q3+fwg534bqcBar0Je+Yz8reo7GlV0ynzwAgsAo5WCZShZcQJZVdu0+h90u07JFZYKD/V9hnTiZwV13z8BulwuMstEoct89bVm5Ko4Dh/xMYvuXCLIaee6ZbnTqUJ1bh/1Cbq4T9V/4njVtEsm3027DYBCx2Vys23AKh0OhY/tqVLxMFckydh1kaeeRKPmFo3UMwUGoLvlfiYip2KEF/Tb+fsXn/bfQDf41iGyzMzuyI3Ku9xR4f7BER3DbufUFv6uyzMnp84n7fjaCKFJn/FBqjBzodwGWsiThbDbffr+dXbsTqVjRysmTmeTnu1A1rUx12EuCP1nH5ctbyM52oKlaEaVlyhajUXTnTFgNWMwGvvt6CNWrXVlp3/WjJnLqt0XeT553tZQJooi5YnnknDyE8xFwqtPldV+gUtc23LB2etnM+x9Al0e+BjFYLbR45wl2/O/dEq2apCArTV54gNy4M2TsPkRw7Rj2vPAxSas3F4THpW7eTfy8lXT5bTLgdjvlnz6HpihYY6KxRhX9aF8aqlYJ46Xne6JpGjcPmU5aen6Z6q+XBqNRQhAEnE7f1Z4yMz3j0S8nBoPA/fe2Iz/fRUxMGH371MVqLZ2+UqDE/fgnZ2Yv93pOPF/gR1XKxp1lCLLQd8NviEYDzswcwhrVZn7dvgTZAWIAABfqSURBVORfkjUrBVupO6FsZEeuBXSD/x+m/kN3ElqvJtufeIecg3HuKlpFIQqIRiOS1UzjZycQP28F2x97k0LlDS/yTSt5NhLmryJlww6OfvEbp35diCafX11LIlG9OtD1948va4GICzr+/hh7SRSoV68ihw6nFt+4FISFmvly6s3cPWFOkZvTV5JmsZW5e0yrf21+TVXZ9shrPhcfoiTS5IUH2PPq/6EVkTXubixiqhCGMz3TZ+XNXsu+I7RuDQCC3f/Qbe5nrOg1Bk2WUV0ygiQSM7g3NW8PPLv8WkV36VxDrB50H2eXrgOnd5dHdL8utJ/2OvELVnN48nfkHvMvC9YQGoxic/xj7M8jSBKVurSmz2rf1bJKy+YtZ3jq2SXk5nkaktimkURHhbL/QDIREUHcPboV3brW5IefdzL1883uaxSEguSmC4ii+7ivuP/GjSox5aObWL0mjm07z7Jm7Qns9n9W82azOwHNZnMF/NQhioJPjXpvhIWZGX1HC375bQ+ZF6lhXky7NlX5v49vwmgseQKc4nJx8P1viP9zOXKeDXtSKs70LELqVKfVB08TM9h3NSrF4WRBg/7knfKeWwDQ8cf3qHXXYBa3HUr6tr3eG4kClqgI6j0wiroP3M6cyI5efWeiycjg06u9PmHK+Tbi563EkZxGZPd2VGjesPgXf42h+/CvExSnkwPvfsWBt7/0CHuTgix0/vUjtj38Gs70LK+qmyVBtJjpOuv/iO7dEclc9qFveXlO+tz4vYegmsVi4NGHOjJyeKzXfna7zPYdCQiiQG6uk7feXYPsUpEVlfr1InhjUm/e+3AdmzefKYi4MZkk6tWtyHdf3VaowMeUTzcy/bfdHglagWI0SsQ2jeTAwWTs9qKfxkRRoEnjSH74xq3VfvZsNi+8vJwDB5NRNQ2r1UibVlW48/YWpVKfBHBmZjO3Vm9cmdlez0tBFrrMmELVAd7lqXc88TaHPvq+yDkkq4V2X71B4ooNbqkQb3bnvI9fCrJiiQwntGFtEhf/7dEstG4Nbjqy5LIEG1wL6Ab/OkOxO9j68Guc/HmeO26/QhhtprzIuSXriPthjn9qggEgBVkQJIkW7z5J5Rs6oykqwTWrltkN4I9Ze/l4ykYcDvcq22IxEFM1jB++HVJI8rcoZFnl5KkMgoNNhQTKNm46zaw5B8jLd9K/bz0G9K/vsVIeOPgnziWWXG0U4LWXezFwQANkWWX0uFmcPJVRICdsMUtUrBhEWpoNBLexL1/Owtdf3uKhP3NB875CBd+JXYGydsjDxM9eVmSbcs3q0+SZCeQcP02FZg2oMrAHosHtEZ4Z3hZnhvebxcVIQRa6z/+CNYPu94jiuRTRZKT2uCHE/7kCZ0YWqsOJYDIgGU30WPwVkV282jMddIN/3SLb7Liyc7FUCkcQRWZV6oAj1bNQRZkhnP/f+c9URJdWdPl1MkEx0aUees/eRP6YuY/09Hx69qjNoIENr1iVqkG3/kzC2eINmi/MJomNf99X8LvN7mLmrH0sXnoMq9XIsCFN6NunLsfj0tm3P5noqBDatql6xbT5f7PG+pXPYQgJQs6zIQW5s8Q1WUE0Gb26+7xhDAuh48/vk3P0FLtfmOzWl1I15DzvkWbBtWK4ccccjn8zk+S1WwlrUIt6D94esPjf9YZu8HUAmFOt+/nybr4p36oxmTsOlNmc1pgoBset8FqRy5aYwo7/vU383BUIkkTN22+ixbtPYSrnW1Xx3+Crb7by7Q87ChX48FfKRRQFevWozXtv9yu64b/I70HNLm/m63mMYSF0mfEJlft2wZGWQdLqLWiywobRT3vdyC3frAEDds+77Nd1rVGUwdfVMq8j6t47HMlq9npOkCRqjh6MuZJ3xcOSYotP4veg5syJ6cbBD79117MF8hNTWNDwRk79tggl346ck0fcd3NY0eMurrZFyOg7W9K4USRWqwGDQSQoyEj58lY+eKc/rVtVwWyWqFQpmOFDmxZSFDUZRUKCTTz6cId/9fqdmdkc/fI39k76lMTlGzz+vlVv8b0hGxDF+dRFkcge7QAwV6xA9SH9qDFiABFtYxGkwm40KchK/YfvLJvr0ilAD8u8jmj87L2kbd5F0uot7g0vQcBSuRJt/u8lIto1w1ShHNsee4OkZRt86w5IkqdOcTFosoItIYk9L08h7/Q5Gv1vLAtjByHn5BVqpzqd5Bw7xYH3v+bU9PlkHTyOtXIksa88TLVb+3Ds6xkkLl0PokB0747UHjcES0TZ3qC8YTYb+PqLW9ix8yz7DyQTFRVCj261MJsN9OxRu1DbkcNjmf7Lbk6cyqB5bGVGjoilUkRwQPPZk9OwnUshtG51DMGly4hN27qHFX3GoskqSr4NQ0gQ4W1j6bn46wJ9GWtUJbc7rpT3WXNkOI4k78V+RJOR7vM+96pp0/mPj1nZeyz58YkgCKgumRojB1Bn/NDSXZCOB7pL5zokY/chMnYeILhWDJHd2haKdsg+epK/WtziqXciCgiC6F4dlkJYXbSYie7dkbOLVns1MBeyJi/eWJas7k1hxeEodFw0m2j35WvkHD1J/J/LMYWXo8FjY6h2W9//ZASHnG9j45hnSJi/CtFkRFMUmr70EE2enVCi8ZI37GBV33EeOvNSkIUWbz9Bg0dHo6kqM8Ja+/SjB0KN2wcRf0kRE3C/p/23zqRCc9+lPjVNI3XjTmwJSYS3jdX99P/f3plHR1Xlefzze6/2JJBASAIJIUjYEkGCCDTYGgUVRUFobHFmVE4rSB+n1W7nIMLYttoto45KNy6NMCo2I66oOIgLsquIzRpCJGyyJQQIm5AQUlV3/qgiJKQqVSGkEqj7OadO3nLrvd+7efV99/3u7/5uA9A+fE292LfoO1b+ZhIVJaUor5ek3L5kT7qPJTfd1+DEV5a4mKBD4IHw8hacVV6sliofsBnjpOsDd9Lr6YcbZGdjc3hdAWsnPEfpqg04khPJnjye4i9XsOfDL/FUm/TDjHHSb+ZfaJXTHeXx0qJ7JzzlJ9n94Rf8vG03rXK614iYOU3Bi2+wftILQTtj4y/rxk3rPsFdVs77cb2rXG3njAjDdy1h89RZbHn1bV+fgAim3UbWo+Po8dj9DTu+Jmy04GvqjVKK8qL9WGKc2OJbUFZUwqeZ19dqvZ3GEhtD+q+HsP3NuSHzlNdb1OuJ4bBx684lOJJah/2d4q++YeuM9/CUldNh9FA6jB5aS0Tr4vhPe6g4eJj4S7tgOgL3k5zmyMZCvuj/6xotb8PpQJ2qDDha2rBZEYsFBKxxMXgrTuGtdOM+XoYl1oUrvR3XfzOnKm12RelhPkq7qs7Im4Re3blx7cds+NM0Nj75UsC3rdPROKFCKAH6vTGFTmNGAnBw1QZ2vjsfwzTpcMfNtMrJCvl9zflD59LR1BsRwZWaXLVuT0xALIFHcjpTk7lh5Xu40lIoXZXH0Y2FdR88iNgbdlvDJsXwY9pslP6QR+rQ3LDKr3v0v9k8bXaVAJcsWcWOWR+R+/n/YJh1j149ub+UZSPu5/DaTYjVCl4vvadOIvOe286UOXiIwmmzKVn8PbGZ6ZzYVVRLRL1BHqTgSwqG/w3mbPeM+3gZx7fuZMNjU+kz7Y8A7F++GsNmCyr4pstBp3tHcWzzdgqemRFY7B12cj+bwZG8zRROm83xbbtQARKfGXYbNxcsILbjGRdMYt+eJPbtWauspunRgq8JC9NmI+uRseRPmV5DdEyng4FvP48rLYXyfQf4ectP4R1QwLDZMKwWlNuDp+JUULEXq8UX5x3mS4HyenGm+Ibde91uSldtABFa+6NBDq/J58TOIhJ6ZyEi/Dj1rRpzp3pOlHNw5XqK5i8h9ZZrKf1+PWV7S4jv2ZXyvSWgFIkDe2PabCy9ZTyH1m7y9S34QxtXP/AXnG2TcKYkYtisfD1oDJXHjuM9WcHBb9f43Cfn8QXHe6qSne98ViX41hYxQV00Ypq0+WUfMsfdzo9TZ+EN1AEvwiVjbyP56r4kX92XFl06snzUA7U62cVqIWvi2Bpir2neaMHXhE32pPFYW8SS//R0Tu4vpWX3S+j9wqMkXXUFAKcOH8OwWsJrpSvI/s/fYmsZx9oJz9bp4kkckMOBpT+EZaOYJq70tiT0zqZk8UqW3/Yg3ko3KIVYTKyxLk6WlCKmrwO6Ve9s33R5Z+E+XsaWv89h3cTnKdtdjPJ6fa1y08C02zGsFnq/MJEjeYW1Ri57yspZest4rLEuKk+U+Vxc/usL1Eo+H7jLT7Lw2rsQhJ+37sQTJG228no5tHYTG6dMx7RYEMOo9ewxbFZiO6RWrScP+gX2VvF4yspr2G/abGSOu70xLkfTSDTIhy8itwF/AroDfZVSAZ3uIjIE+CtgAjOVUv8V6tjah3/h4XW7mZs0wDcVYxgM276QvfOXsm7Cc0H7BsRmRQyjRgu8BoaBNc7XolVuNy2yMvnl3JewuBzMyxgUMvpErBYQCZzBMUSoomG3YdistVq+TYFYTJQ7/HBZsVqxtW5J5ZFjtVw/psPOzT8uIKaa6J/YVcSK2x/i8NoCxBAcKW0YMPs52gxougydmsA0pg9/IzASmF7HyU3gZeA6YA/wg4jMU0qdv+GcmmaBYbHQ55XH+f6eSb4oDaWCdtDGZqYT27E9lUeO4anjjUCdqqzT+2G6nIzc/y1bp79L3pMvc3h1PvM6XBO0v6HW8SvdvvSZAXfW/V1vkE7WcBCLiVitdfruzxQ+yxZDMF1OPGXlvha6x1svsQdQlZVUHj5G8qD+lCz6vmqSG+X1cvm0x2qIPUBMejtu+O49yksO4j1ZgSu93QUZ+hrtNEjwlVIFQKh/fF9gq1Jqu7/sO8BwQAv+RUjG6KHEdkzjxxfe4MTOIuJ7dmHnnPl4Tlb4RMkQrHGxDFrsS6mcMngA+VNeCxzuGcbEs616Z1G6agNrJzxbo6VaHwG0tojxTRVZj7TFvpOocwpnFKuFlMEDuPyvk1mQc2utjtja56m5atrtxHZqz7FN23zXeY5v6d6KU5TtKmb4T4vYO28RKEXqsGtxprQJ+p3GnPRG0/hEwoefCuyutr4H6BeB82qaiMR+l3Hlu1Or1ns+9RDbZr7P0YJtJPa7jEvuHoG1RSwArftdRuqwayj6dPGZlM0Wk7jOGZzYtgvvqeCCarocdP3dv5H3xEsNmsy9zZWXs2/hd8HdRnVR34cEoDwe+r3+NK6UNgxe/BbLRz1A2a7i0F/04yk/ydH1m+t93kA42rTCmZxI5lg9K1Q0EFLwRWQhECjd4WSl1Cfn0xgRGQeMA0hPTz+fh9Y0Ic7kRC6d/NuA+0SEgf/7PLvnfsn2Nz9CROg4ZgT21vEsu/V+X0hiAAy7nZRBA0gbcR1r/hCySyg4hkHO8xNZ0COCsyJ5FaUr1+O6dTAxGWn1dsfUG9ME5a31cDJjnHT9/ZjGPbemWRFS8JVSDc2stBdoX209zb8t0LleA14DX6dtA8+ruUAQwyB91BDSRw2p2nb8pz1Bo32cqcn0f/1pUgYPQAyDhJzuteYyPXNwoWV2JmaLWI7lFdboYBWbhdxPp9OyS0faDc2laP6SoA+YWoc1TZTXc87hlT9v2QlA/pTpjZqyuv2vrqfPS39k+6yPKZw2m4rSQ5h2G96KSrInjSftlmsb7dya5kckXDo/AJ1FpCM+oR8N/EsEzqu5gInNSCPp6isoWbKqhvCbLge5/zedhF5n8rL0fOIBir9cEditoxTHd+wBpWqkLBCLSXx2F5Jy+7Lr/QVYW8bhbJdEefEBxDDwnKzAcNpRFbU7Zg2blcz7RlP48uxz9p+3u+FKAPbO+zrsh8wZ4wN3hFv8o3ANhw1V6aHbH8bQ86mHEBGyHxlL9iNjObppKydLDpKQk1U1MlcTPTQoPbKIjBCRPcAvgPki8oV/ezsR+QxAKeUG/h34AigA3lNK5TfMbE00cOUHf6P9iOt84Y92G670tvzyw2k1xB58aQIGL5lNfI8u/klYauI5Ue6Loa/m0lBuD8cKdzC/x82s/M0kdrw5l/LiA2AIPZ74HSP3fUNsRlrgVAd2Gy0v7YzFdW6zTsV160h8z64AWOsjuqZBXOcOdJ9wb1Xag6pdLge5819jRPEKrl8xh5H7v+WyP/++VkBFy6xMkq/pr8U+StG5dDTNHveJMiqPl+FIah0yFHDLq2+z5uFngsb1V+d0fpqzB045khMZUbSchdfcyYFlte9B02FnaMFnfHPHwxxZV1D15mDYbZhOu6/zWUHLrE4kDerP1lfmoNy+6RnbDOxN7oIZWP1pj3fM/oRV4x+vEakjFgvxOd1J6NWNnzfvILF/L7IeGYu9VXxVmZKlq8h7fBrHCncQ36MrPZ96UKcz0AA6l47mAscS4wo7L7zpclbFlIdCeT0Bo2zcJ8o4mr+Fbg+N4dDq/LPE2CQhJ4vYjDQGLZrFpmdmsGOWb1LujDuHkz1xHGKaeE9VVkUi5Uz5D45v340juXUN0QbI+NdhHFqziS2vvO3zrXs8xGV2IPfTv9cZApl8dV+Sl/wjrOvUaE6jW/iai4pTR47xUepVIdM4i9XiE+YAoZimy8GNaz8mrnMGeU9MY9MzM3xi7PbQonMGuQtm1Bmrfi6U7zvAodX5ONslkdCrux7UpDlndHpkTVRR9PkyVox6EEwDlMJb6abH4/dT/PlyDqxYDWKQess1JF19BesnvVjz4SBCXJcMbi5YUCW6FaWHObRmE86UROJ7dG2iq9JowkMLvibqcJ8oo+jz5Xgr3bS9fmCVK8VbWQkiGBYLyuvl27smsGfuVyAgpgWL086gpf+gZbdOTXwFGs25oQVfo6mDI/lbOPjNGhwpibS78SoMq7WpTdJozhndaavR1EF8dmfiszs3tRkaTaPToDh8jUaj0Vw4aMHXaDSaKEELvkaj0UQJWvA1Go0mStCCr9FoNFFCsw3LFJEDwM5GPEUicLARj38xoOsoNLqO6kbXT2jOdx11UEoFHArebAW/sRGRfwaLVdX40HUUGl1HdaPrJzSRrCPt0tFoNJooQQu+RqPRRAnRLPivNbUBFwC6jkKj66hudP2EJmJ1FLU+fI1Go4k2ormFr9FoNFGFFnyNRqOJEqJG8EXkNhHJFxGviAQNgRKRISKyWUS2isjESNrY1IhIKxH5SkS2+P8mBCnnEZF1/s+8SNsZaULdEyJiF5F3/fu/F5GMyFvZtIRRR2NE5EC1++beprCzqRCR10Vkv4hsDLJfRORv/vrbICK9G8OOqBF8YCMwElgWrICImMDLwI1AFnCHiGRFxrxmwUTga6VUZ+Br/3ogypVSvfyfYZEzL/KEeU/cAxxWSmUCLwLPRNbKpqUev5t3q903MyNqZNPzJjCkjv03Ap39n3HAq41hRNQIvlKqQCm1OUSxvsBWpdR2pdQp4B1geONb12wYDszyL88Cbm1CW5oL4dwT1evtA2CQRNektNH+uwmJUmoZcKiOIsOBt5SPlUC8iLQ933ZEjeCHSSqwu9r6Hv+2aCFZKVXsX94HJAcp5xCRf4rIShG52B8K4dwTVWWUUm7gKNA6ItY1D8L93fzK7674QETaR8a0C4aIaM9FNeOViCwEUgLsmqyU+iTS9jRH6qqj6itKKSUiwWJ2Oyil9orIJcAiEclTSm0737ZqLio+BeYopSpE5D58b0TXNrFNUcdFJfhKqcENPMReoHrLI82/7aKhrjoSkRIRaauUKva/Tu4Pcoy9/r/bRWQJkANcrIIfzj1xusweEbEALYHSyJjXLAhZR0qp6vUxE3g2AnZdSEREe7RLpyY/AJ1FpKOI2IDRwEUfhVKNecDd/uW7gVpvRSKSICJ2/3IiMBDYFDELI08490T1ehsFLFLRNaIxZB2d5Y8eBhRE0L4LgXnAXf5onf7A0Wru1fOHUioqPsAIfH6xCqAE+MK/vR3wWbVyNwGF+Fqsk5va7gjXUWt80TlbgIVAK//2PsBM//IAIA9Y7/97T1PbHYF6qXVPAE8Cw/zLDuB9YCuwCrikqW1uhnU0Bcj33zeLgW5NbXOE62cOUAxU+nXoHmA8MN6/X/BFOm3z/676NIYdOrWCRqPRRAnapaPRaDRRghZ8jUajiRK04Gs0Gk2UoAVfo9FoogQt+BqNRhMlaMHXaDSaKEELvkaj0UQJ/w+kksVCFGug4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize with a plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(x=X[:, 0], \n",
    "            y=X[:, 1], \n",
    "            c=y, \n",
    "            cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h0e1oR27HC76"
   },
   "source": [
    "Alrighty, looks like we've got a problem to solve.\n",
    "\n",
    "Let's find out how we could build a PyTorch neural network to classify dots into red (0) or blue (1).\n",
    "\n",
    "> **Note:** This dataset is often what's considered a **toy problem** (a problem that's used to try and test things out on) in machine learning. \n",
    "> \n",
    "> But it represents the major key of classification, you have some kind of data represented as numerical values and you'd like to build a model that's able to classify it, in our case, separate it into red or blue dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ny6_J7F4HC76"
   },
   "source": [
    "### 1.1 Input and output shapes\n",
    "\n",
    "One of the most common errors in deep learning is shape errors.\n",
    "\n",
    "Mismatching the shapes of tensors and tensor operations with result in errors in your models.\n",
    "\n",
    "We're going to see plenty of these throughout the course.\n",
    "\n",
    "And there's no surefire way to making sure they won't happen, they will.\n",
    "\n",
    "What you can do instead is continaully familiarize yourself with the shape of the data you're working with.\n",
    "\n",
    "I like referring to it as input and output shapes.\n",
    "\n",
    "Ask yourself:\n",
    "\n",
    "\"What shapes are my inputs and what shapes are my outputs?\"\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r0h--vKdHC77",
    "outputId": "16e65bb5-83e1-49c7-af5e-90ecede4eeae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 2), (1000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the shapes of our features and labels\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PW7zolShbfDg"
   },
   "source": [
    "Looks like we've got a match on the first dimension of each.\n",
    "\n",
    "There's 1000 `X` and 1000 `y`. \n",
    "\n",
    "But what's the second dimension on `X`?\n",
    "\n",
    "It often helps to view the values and shapes of a single sample (features and labels).\n",
    "\n",
    "Doing so will help you understand what input and output shapes you'd be expecting from your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k-L5zobVHC78",
    "outputId": "b3a96ca8-45f1-47d1-a98b-c2a7be79c0d5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Values for one sample of X: [0.75424625 0.23148074] and the same for y: 1\n",
      "Shapes for one sample of X: (2,) and the same for y: ()\n"
     ]
    }
   ],
   "source": [
    "# View the first example of features and labels\n",
    "X_sample = X[0]\n",
    "y_sample = y[0]\n",
    "print(f\"Values for one sample of X: {X_sample} and the same for y: {y_sample}\")\n",
    "print(f\"Shapes for one sample of X: {X_sample.shape} and the same for y: {y_sample.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vn692XaRaifl"
   },
   "source": [
    "This tells us the second dimension for `X` means it has two features (vector) where as `y` has a single feature (scalar).\n",
    "\n",
    "We have two inputs for one output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eLyDPN6ZR_ho"
   },
   "source": [
    "### 1.2 Turn data into tensors and create train and test splits\n",
    "\n",
    "We've investigated the input and output shapes of our data, now let's prepare it for being used with PyTorch and for modelling.\n",
    "\n",
    "Specifically, we'll need to:\n",
    "1. Turn our data into tensors (right now our data is in NumPy arrays and PyTorch prefers to work with PyTorch tensors).\n",
    "2. Split our data into training and test sets (we'll train a model on the training set to learn the patterns between `X` and `y` and then evaluate those learned patterns on the test dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z2gcR31aHC78",
    "outputId": "cd4e10c1-c358-4b74-81e0-bab7610197cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.7542,  0.2315],\n",
       "         [-0.7562,  0.1533],\n",
       "         [-0.8154,  0.1733],\n",
       "         [-0.3937,  0.6929],\n",
       "         [ 0.4422, -0.8967]]), tensor([1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn data into tensors\n",
    "# Otherwise this causes issues with computations later on\n",
    "import torch\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# View the first five samples\n",
    "X[:5], y[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r9XNJv8lfmRG"
   },
   "source": [
    "Now our data is in tensor format, let's split it into training and test sets.\n",
    "\n",
    "To do so, let's use the helpful function [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) from Scikit-Learn.\n",
    "\n",
    "We'll use `test_size=0.2` (80% training, 20% testing) and because the split happens randomly across the data, let's use `random_state=42` so the split is reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DW6PU82BHC79",
    "outputId": "a2d3f3df-701e-44ef-a506-fd31d5443e90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 200, 800, 200)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2, # 20% test, 80% train\n",
    "                                                    random_state=42) # make the random split reproducible\n",
    "\n",
    "len(X_train), len(X_test), len(y_train), len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t-wYDWy9gSRc"
   },
   "source": [
    "Nice! Looks like we've now got 800 training samples and 200 testing samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iCyQ93VTHC79"
   },
   "source": [
    "## 2. Building a model\n",
    "\n",
    "We've got some data ready, now it's time to build a model.\n",
    "\n",
    "We'll break it down into a few parts.\n",
    "\n",
    "1. Setting up device agnostic code (so our model can run on CPU or GPU if it's available).\n",
    "2. Constructing a model by subclassing `nn.Module`.\n",
    "3. Defining a loss function and optimizer.\n",
    "4. Creating a training loop (this'll be in the next section).\n",
    "\n",
    "The good news is we've been through all of the above steps before in notebook 01.\n",
    "\n",
    "Except now we'll be adjusting them so they work with a classification dataset.\n",
    "\n",
    "Let's start by importing PyTorch and `torch.nn` as well as setting up device agnostic code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "2itMTezPRnVR",
    "outputId": "507bf4a3-b5ae-4943-aa21-4eb181b0d741"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard PyTorch imports\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "# Make device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p1Fil1v04nXr"
   },
   "source": [
    "Excellent, now `device` is setup, we can use it for any data or models we create and PyTorch will handle it on the CPU (default) or GPU if it's available.\n",
    "\n",
    "How about we create a model?\n",
    "\n",
    "We'll want a model capable of handling our `X` data as inputs and producing something in the shape of our `y` data as ouputs.\n",
    "\n",
    "In other words, given `X` (features) we want our model to predict `y` (label).\n",
    "\n",
    "This setup where you have features and labels is referred to as **supervised learning**. Because your data is telling your model what the outputs should be given a certain input.\n",
    "\n",
    "To create such a model it'll need to handle the input and output shapes of `X` and `y`.\n",
    "\n",
    "Remember how I said input and output shapes are important? Here we'll see why.\n",
    "\n",
    "Let's create a model class that:\n",
    "1. Subclasses `nn.Module` (almost all PyTorch models are subclasses of `nn.Module`).\n",
    "2. Creates 2 `nn.Linear` layers in the constructor capable of handling the input and output shapes of `X` and `y`.\n",
    "3. Defines a `forward()` method containing the forward pass computation of the model.\n",
    "4. Instantiates the model class and sends it to the target `device`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "C_EsAE3VHC7-",
    "outputId": "d9c976ea-e23f-4993-c847-a15b0bf7b0b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV0(\n",
       "  (layer_1): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (layer_2): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Construct a model class that subclasses nn.Module\n",
    "class CircleModelV0(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # 2. Create 2 nn.Linear layers capable of handling X and y input and output shapes\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=5) # takes in 2 features (X), produces 5 features\n",
    "        self.layer_2 = nn.Linear(in_features=5, out_features=1) # takes in 5 features, produces 1 feature (y)\n",
    "    \n",
    "    # 3. Define a forward method containing the forward pass computation\n",
    "    def forward(self, x):\n",
    "        # Return the output of layer_2, a single feature, the same shape as y\n",
    "        return self.layer_2(self.layer_1(x)) # computation goes through layer_1 first then the output of layer_1 goes through layer_2\n",
    "\n",
    "# 4. Create an instance of the model and send it to target device\n",
    "model_0 = CircleModelV0().to(device)\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TtdkMniZ7KyK"
   },
   "source": [
    "What's going on here?\n",
    "\n",
    "We've seen a few of these steps before.\n",
    "\n",
    "The only major change is what's happening between `self.layer_1` and `self.layer_2`.\n",
    "\n",
    "`self.layer_1` takes 2 input features `in_features=2` and produces 5 output features `out_features=5`.\n",
    "\n",
    "This is known as having 5 **hidden units** or **neurons**.\n",
    "\n",
    "This layer turns the input data from having 2 features to 5 features.\n",
    "\n",
    "Why do this?\n",
    "\n",
    "This allows the model to learn patterns from 5 numbers rather than just 2 numbers, *potentially* leading to better outputs.\n",
    "\n",
    "I say potentially because sometimes it doesn't work.\n",
    "\n",
    "The number of hidden units you can use in neural network layers is a **hyperparameter** (a value you can set yourself) and there's no set in stone value you have to use.\n",
    "\n",
    "Generally more is better but there's also such a thing as too much. The amount you choose will depend on your model type and dataset you're working with. \n",
    "\n",
    "Since our dataset is small and simple, we'll keep it small.\n",
    "\n",
    "The only rule with hidden units is that the next layer, in our case, `self.layer_2` has to take the same `in_features` as the previous layer `out_features`.\n",
    "\n",
    "That's why `self.layer_2` has `in_features=5`, it takes the `out_features=5` from `self.layer_1` and performs a linear computation on them, turning them into `out_features=1` (the same shape as `y`).\n",
    "\n",
    "![A visual example of what a classification neural network with linear activation looks like on the tensorflow playground](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-linear-activation.png)\n",
    "*A visual example of what a similar classificiation neural network to the one we've just built looks like. Try create one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "You can also do the same as above using [`nn.Sequential`](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html).\n",
    "\n",
    "`nn.Sequential` performs a forward pass computation of the input data through the layers in the order they appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H7I8GyLjHC7_",
    "outputId": "def65504-863a-4361-816e-909dbfa4d624"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=2, out_features=5, bias=True)\n",
       "  (1): Linear(in_features=5, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replicate CircleModelV0 with nn.Sequential\n",
    "model_0 = nn.Sequential(\n",
    "    nn.Linear(in_features=2, out_features=5),\n",
    "    nn.Linear(in_features=5, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MiRHq9mxFIf-"
   },
   "source": [
    "Woah, that looks much simpler than subclassing `nn.Module`, why not just always use `nn.Sequential`?\n",
    "\n",
    "`nn.Sequential` is fantastic for straight-forward computations, however, as the namespace says, it *always* runs in sequential order.\n",
    "\n",
    "So if you'd something else to happen (rather than just straight-forward sequential computation) you'll want to define your own custom `nn.Module` subclass.\n",
    "\n",
    "Now we've got a model, let's see what happens when we pass some data through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tt339-_CC8sz",
    "outputId": "a4014167-4181-434e-ab75-905094229b3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of predictions: 200, Shape: torch.Size([200, 1])\n",
      "Length of test samples: 200, Shape: torch.Size([200])\n",
      "\n",
      "First 10 predictions:\n",
      "tensor([[0.7311],\n",
      "        [0.7607],\n",
      "        [0.4336],\n",
      "        [0.8163],\n",
      "        [0.0846],\n",
      "        [0.1053],\n",
      "        [0.4653],\n",
      "        [0.3110],\n",
      "        [0.4488],\n",
      "        [0.7589]], device='cuda:0', grad_fn=<SliceBackward0>)\n",
      "\n",
      "First 10 test labels:\n",
      "tensor([1., 0., 1., 0., 1., 1., 0., 0., 1., 0.])\n"
     ]
    }
   ],
   "source": [
    "# Make predictions with the model\n",
    "untrained_preds = model_0(X_test.to(device))\n",
    "print(f\"Length of predictions: {len(untrained_preds)}, Shape: {untrained_preds.shape}\")\n",
    "print(f\"Length of test samples: {len(y_test)}, Shape: {y_test.shape}\")\n",
    "print(f\"\\nFirst 10 predictions:\\n{untrained_preds[:10]}\")\n",
    "print(f\"\\nFirst 10 test labels:\\n{y_test[:10]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7v8TVnqGMZh"
   },
   "source": [
    "Hmm, it seems there's the same amount of predictions as there is test labels but the predictions don't look like they're in the same form or shape as the test labels.\n",
    "\n",
    "We've got a couple steps we can do to fix this, we'll see these later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8aoQn39pHC7_"
   },
   "source": [
    "### 2.1 Setup loss function and optimizer\n",
    "\n",
    "We've setup a loss (also called a criterion or cost function) and optimizer before in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/#creating-a-loss-function-and-optimizer-in-pytorch).\n",
    "\n",
    "But different problem types require different loss functions. \n",
    "\n",
    "For example, for a regression problem (predicting a number) you might used mean absolute error (MAE) loss.\n",
    "\n",
    "And for a binary classification problem (like ours), you'll often use [binary cross entropy](https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a) as the loss function.\n",
    "\n",
    "However, the same optimizer function can often be used across different problem spaces.\n",
    "\n",
    "For example, the stochastic gradient descent optimizer (SGD, `torch.optim.SGD()`) can be used for a range of problems, so can too the Adam optimizer (`torch.optim.Adam()`). \n",
    "\n",
    "| Loss function/Optimizer | Problem type | PyTorch Code |\n",
    "| ----- | ----- | ----- |\n",
    "| Stochastic Gradient Descent (SGD) optimizer | Classification, regression, many others. | [`torch.optim.SGD()`](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html) |\n",
    "| Adam Optimizer | Classification, regression, many others. | [`torch.optim.Adam()`](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html) |\n",
    "| Binary cross entropy loss | Binary classification | [`torch.nn.BCELossWithLogits`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) or [`torch.nn.BCELoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) |\n",
    "| Cross entropy loss | Mutli-class classification | [`torch.nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) |\n",
    "| Mean absolute error (MAE) or L1 Loss | Regression | [`torch.nn.L1Loss`](https://pytorch.org/docs/stable/generated/torch.nn.L1Loss.html) | \n",
    "| Mean squared error (MSE) or L2 Loss | Regression | [`torch.nn.MSELoss`](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss) |  \n",
    "\n",
    "*Table of various loss functions and optimizers, there are more but these some common ones you'll see.*\n",
    "\n",
    "Since we're working with a binary classification problem, let's use a binary cross entropy loss function.\n",
    "\n",
    "> **Note:** Recall a **loss function** is what measures how *wrong* your model predictions are, the higher the loss, the worse your model.\n",
    ">\n",
    "> Also, PyTorch documentation often refers to loss functions as \"loss criterion\" or \"criterion\", these are all different ways of describing the same thing.\n",
    "\n",
    "PyTorch has two binary cross entropy implementations:\n",
    "1. [`torch.nn.BCELoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCELoss.html) - Creates a loss function that measures the binary cross entropy between the target (label) and input (features).\n",
    "2. [`torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) - This is the same as above except it has a sigmoid layer ([`nn.Sigmoid`](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html)) built-in (we'll see what this means soon).\n",
    "\n",
    "Which one should you use? \n",
    "\n",
    "The [documentation for `torch.nn.BCEWithLogitsLoss()`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) states that it's more numerically stable than using `torch.nn.BCELoss()` after a `nn.Sigmoid` layer. \n",
    "\n",
    "So generally, implementation 2 is a better option. However for advanced usage, you may want to separate the combination of `nn.Sigmoid` and `torch.nn.BCELoss()` but that is beyond the scope of this notebook.\n",
    "\n",
    "Knowing this, let's create a loss function and an optimizer. \n",
    "\n",
    "For the optimizer we'll use `torch.optim.SGD()` to optimize the model parameters with learning rate 0.1.\n",
    "\n",
    "> **Note:** There's a [discussion on the PyTorch forums about the use of `nn.BCELoss` vs. `nn.BCEWithLogitsLoss`](https://discuss.pytorch.org/t/bceloss-vs-bcewithlogitsloss/33586/4). It can be confusing at first but as with many things, it becomes easier with practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "DjpQsdOZHC7_"
   },
   "outputs": [],
   "source": [
    "# Create a loss function\n",
    "# loss_fn = nn.BCELoss() # BCELoss = no sigmoid built-in\n",
    "loss_fn = nn.BCEWithLogitsLoss() # BCEWithLogitsLoss = sigmoid built-in\n",
    "\n",
    "# Create an optimizer\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), \n",
    "                            lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RKmi3fp9wYnV"
   },
   "source": [
    "Now let's also create an **evaluation metric**.\n",
    "\n",
    "An evaluation metric can be used to offer another perspective on how your model is going.\n",
    "\n",
    "If a loss function measures how *wrong* your model is, I like to think of evaluation metrics as measuring how *right* it is.\n",
    "\n",
    "Of course, you could argue both of these are doing the same thing but evaluation metrics offer a different perspective.\n",
    "\n",
    "After all, when evaluating your models it's good to look at things from multiple points of view.\n",
    "\n",
    "There are several evaluation metrics that can be used for classification problems but let's start out with **accuracy**.\n",
    "\n",
    "Accuracy can be measured by dividing the total number of correct predictions over the total number of predictions.\n",
    "\n",
    "For example, a model that makes 99 correct predictions out of 100 will have an accuracy of 99%.\n",
    "\n",
    "Let's write a function to do so.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RcaZQR0mHC7_"
   },
   "outputs": [],
   "source": [
    "# Calculate accuracy (a classification metric)\n",
    "def accuracy_fn(y_true, y_pred):\n",
    "    correct = torch.eq(y_true, y_pred).sum().item() # torch.eq() calculates where two tensors are equal\n",
    "    acc = (correct / len(y_pred)) * 100 \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OuplloDPxviL"
   },
   "source": [
    "Excellent! We can now use this function whilst training our model to measure it's performance alongside the loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4UpJKZ8PHC8A"
   },
   "source": [
    "## 3. Train model\n",
    "\n",
    "Okay, now we've got a loss function and optimizer ready to go, let's train a model.\n",
    "\n",
    "Do you remember the steps in a PyTorch training loop?\n",
    "\n",
    "If not, here's a reminder.\n",
    "\n",
    "Steps in training:\n",
    "\n",
    "<details>\n",
    "    <summary>PyTorch training loop steps</summary>\n",
    "    <ol>\n",
    "        <li><b>Forward pass</b> - The model goes through all of the training data once, performing its\n",
    "            <code>forward()</code> function\n",
    "            calculations (<code>model(x_train)</code>).\n",
    "        </li>\n",
    "        <li><b>Calculate the loss</b> - The model's outputs (predictions) are compared to the ground truth and evaluated\n",
    "            to see how\n",
    "            wrong they are (<code>loss = loss_fn(y_pred, y_train</code>).</li>\n",
    "        <li><b>Zero gradients</b> - The optimizers gradients are set to zero (they are accumulated by default) so they\n",
    "            can be\n",
    "            recalculated for the specific training step (<code>optimizer.zero_grad()</code>).</li>\n",
    "        <li><b>Perform backpropagation on the loss</b> - Computes the gradient of the loss with respect for every model\n",
    "            parameter to\n",
    "            be updated (each parameter\n",
    "            with <code>requires_grad=True</code>). This is known as <b>backpropagation</b>, hence \"backwards\"\n",
    "            (<code>loss.backward()</code>).</li>\n",
    "        <li><b>Step the optimizer (gradient descent)</b> - Update the parameters with <code>requires_grad=True</code>\n",
    "            with respect to the loss\n",
    "            gradients in order to improve them (<code>optimizer.step()</code>).</li>\n",
    "    </ol>\n",
    "</details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NMeDqPpsnst6"
   },
   "source": [
    "### 3.1 Going from raw model outputs to predicted labels (logits -> prediction probabilities -> prediction labels)\n",
    "\n",
    "Before we the training loop steps, let's see what comes out of our model during the forward pass (the forward pass is defined by the `foward()` method).\n",
    "\n",
    "To do so, let's pass the model some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5stsbDl0zKl",
    "outputId": "829a0842-0a37-455a-b058-3e547200f836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7311],\n",
       "        [0.7607],\n",
       "        [0.4336],\n",
       "        [0.8163],\n",
       "        [0.0846]], device='cuda:0', grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the frist 5 outputs of the forward pass on the test data\n",
    "y_logits = model_0(X_test.to(device))[:5]\n",
    "y_logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b1sNl8D8fWXm"
   },
   "source": [
    "Since our model hasn't been trained, these outputs are basically random.\n",
    "\n",
    "But *what* are they?\n",
    "\n",
    "They're the output of our `forward()` method.\n",
    "\n",
    "Which implements two layers of `nn.Linear()` which internally calls the following equation:\n",
    "\n",
    "$$\n",
    "\\mathbf{y} = x \\cdot \\mathbf{Weights}^T  + \\mathbf{bias}\n",
    "$$\n",
    "\n",
    "The *raw outputs* (unmodified) of this equation ($\\mathbf{y}$) and in turn, the raw outputs of our model are often referred to as [**logits**](https://datascience.stackexchange.com/a/31045).\n",
    "\n",
    "That's what our model is outputing above when it takes in the input data ($x$ in the equation or `X_test` in the code), logits.\n",
    "\n",
    "However, these numbers are hard to interpret.\n",
    "\n",
    "We'd like some numbers that are comparable to our truth labels.\n",
    "\n",
    "To get our model's raw outputs (logits) into such a form, we can use the [sigmoid activation function](https://pytorch.org/docs/stable/generated/torch.sigmoid.html).\n",
    "\n",
    "Let's try it out.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QGC7UmBi0s6E",
    "outputId": "3564ee3b-e34f-40a3-a40a-b9c9b948da04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6751],\n",
       "        [0.6815],\n",
       "        [0.6067],\n",
       "        [0.6935],\n",
       "        [0.5211]], device='cuda:0', grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use sigmoid on model logits\n",
    "y_pred_probs = torch.sigmoid(y_logits)\n",
    "y_pred_probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HxmWz_GVjZoB"
   },
   "source": [
    "Okay, it seems like the outputs now have some kind of consistency (even though they're still random).\n",
    "\n",
    "They're now in the form of **prediction probabilities** (I usually refer to these as `y_pred_probs`), in other words, the values are now how much the model thinks the data point belongs to one class or another.\n",
    "\n",
    "In our case, since we're dealing with binary classification, our ideal outputs are 0 or 1.\n",
    "\n",
    "So these values can be viewed as a decision boundary.\n",
    "\n",
    "The closer to 0, the more the model thinks the sample belongs to class 0, the closer to 1, the more the model thinks the sample belongs to class 1.\n",
    "\n",
    "More specificially:\n",
    "* If `y_pred_probs` >= 0.5, `y=1` (class 1)\n",
    "* If `y_pred_probs` < 0.5, `y=0` (class 0)\n",
    "\n",
    "To turn our prediction probabilities in prediction labels, we can round the outputs of the sigmoid activation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "naZxlTTwlMwX",
    "outputId": "6134e47d-bbb2-46c3-e2ec-b4f42d517e39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([True, True, True, True, True], device='cuda:0')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 1., 1.], device='cuda:0', grad_fn=<SqueezeBackward0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the predicted labels (round the prediction probabilities)\n",
    "y_preds = torch.round(y_pred_probs)\n",
    "\n",
    "# In full\n",
    "y_pred_labels = torch.round(torch.sigmoid(model_0(X_test.to(device))[:5]))\n",
    "\n",
    "# Check for equality\n",
    "print(torch.eq(y_preds.squeeze(), y_pred_labels.squeeze()))\n",
    "\n",
    "# Get rid of extra dimension\n",
    "y_preds.squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5cMsgFWWmPLU"
   },
   "source": [
    "Excellent! Now it looks like our model's predictions are in the same form as our truth labels (`y_test`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MaQ0CN4ZmU1W",
    "outputId": "6b1cd7b4-f1d0-49b5-a8c3-338cf75c6cb0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 0., 1., 0., 1.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NXqUulG3maPH"
   },
   "source": [
    "This means we'll be able to compare our models predictions to the test labels to see how well it's going. \n",
    "\n",
    "To recap, we converted our model's raw outputs (logits) to predicition probabilities using a sigmoid activation function.\n",
    "\n",
    "And then converted the prediction probabilities to prediction labels by rounding them.\n",
    "\n",
    "> **Note:** The use of the sigmoid activation function is often only for binary classification logits. For multi-class classification, we'll be looking at using the [softmax activation function](https://pytorch.org/docs/stable/generated/torch.nn.Softmax.html) (this will come later on).\n",
    ">\n",
    "> And the use of the sigmoid activation function is not required when passing our model's raw outputs to the `nn.BCEWithLogitsLoss` (the \"logits\" in logits loss is because it works on the model's raw logits output), this is because it has a sigmoid function built-in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Va7gg8yxn6Sg"
   },
   "source": [
    "### 3.2 Building a training and testing loop\n",
    "\n",
    "Alright, we've discussed how to take our raw model outputs and convert them to prediction labels, now let's build a training loop.\n",
    "\n",
    "Let's start by training for 100 epochs and outputing the model's progress every 10 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DFABVGo2HC8A",
    "outputId": "e0341074-b603-41d5-a389-c401b4934d73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.72095, Accuracy: 50.00% | Test loss: 0.72767, Test acc: 50.50%\n",
      "Epoch: 10 | Loss: 0.70546, Accuracy: 53.87% | Test loss: 0.71203, Test acc: 52.50%\n",
      "Epoch: 20 | Loss: 0.70011, Accuracy: 52.25% | Test loss: 0.70601, Test acc: 49.50%\n",
      "Epoch: 30 | Loss: 0.69792, Accuracy: 51.50% | Test loss: 0.70315, Test acc: 49.50%\n",
      "Epoch: 40 | Loss: 0.69682, Accuracy: 51.12% | Test loss: 0.70148, Test acc: 48.50%\n",
      "Epoch: 50 | Loss: 0.69613, Accuracy: 50.75% | Test loss: 0.70034, Test acc: 49.50%\n",
      "Epoch: 60 | Loss: 0.69565, Accuracy: 51.00% | Test loss: 0.69948, Test acc: 49.50%\n",
      "Epoch: 70 | Loss: 0.69527, Accuracy: 50.75% | Test loss: 0.69880, Test acc: 50.00%\n",
      "Epoch: 80 | Loss: 0.69497, Accuracy: 50.62% | Test loss: 0.69825, Test acc: 49.50%\n",
      "Epoch: 90 | Loss: 0.69472, Accuracy: 50.62% | Test loss: 0.69779, Test acc: 49.50%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "# Build training and evaluation loop\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_0.train()\n",
    "\n",
    "    # 1. Forward pass (model outputs raw logits)\n",
    "    y_logits = model_0(X_train).squeeze() # squeeze to remove extra `1` dimensions, this won't work unless model and data are on same device \n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # turn logits -> pred probs -> pred labls\n",
    "  \n",
    "    # 2. Calculate loss/accuracy\n",
    "    # loss = loss_fn(torch.sigmoid(y_logits), # Using nn.BCELoss you need torch.sigmoid()\n",
    "    #                y_train) \n",
    "    loss = loss_fn(y_logits, # Using nn.BCEWithLogitsLoss works with raw logits\n",
    "                   y_train) \n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred) \n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_0.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_0(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "It1Xy_f5perA"
   },
   "source": [
    "Hmm, what do you notice about the performance of our model?\n",
    "\n",
    "It looks like it went through the training and testing steps fine but the results don't seem to have moved too much.\n",
    "\n",
    "The accuracy barely moves above 50% on each data split.\n",
    "\n",
    "And because we're working with a balanced binary classification problem, it means our model is performing as good as random guessing (with 500 samples of class 0 and class 1 a model predicting class 1 every single time would achieve 50% accuracy)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WCeyddo-HC8A"
   },
   "source": [
    "## 4. Make predictions and evaluate the model\n",
    "\n",
    "From the metrics it looks like our model is random guessing.\n",
    "\n",
    "How could we investigate this further?\n",
    "\n",
    "I've got an idea.\n",
    "\n",
    "The data explorer's motto!\n",
    "\n",
    "\"Visualize, visualize, visualize!\"\n",
    "\n",
    "Let's make a plot of our model's predictions, the data it's trying to predict on and the decision boundary it's creating for whether something is class 0 or class 1.\n",
    "\n",
    "To do so, we'll write some code to download and import the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) from the [Learn PyTorch for Deep Learning repo](https://github.com/mrdbourke/pytorch-deep-learning).\n",
    "\n",
    "It contains a helpful function called `plot_decision_boundary()` which creates a NumPy meshgrid to visually plot the different points where our model is predicting certain classes.\n",
    "\n",
    "We'll also import `plot_predictions()` which we wrote in notebook 01 to use later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QakJVGI8t2gB",
    "outputId": "4f63a8a3-5eae-49fb-e17e-df6c5721ab5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading helper_functions.py\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path \n",
    "\n",
    "# Download helper functions from Learn PyTorch repo (if not already downloaded)\n",
    "if Path(\"helper_functions.py\").is_file():\n",
    "  print(\"helper_functions.py already exists, skipping download\")\n",
    "else:\n",
    "  print(\"Downloading helper_functions.py\")\n",
    "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
    "  with open(\"helper_functions.py\", \"wb\") as f:\n",
    "    f.write(request.content)\n",
    "\n",
    "from helper_functions import plot_predictions, plot_decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "bEbDUTKjHC8B",
    "outputId": "aa344fe8-0207-43df-f0e9-61b9302459a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uxZpYi4UQbViJIXYAKeXPgUexsq0PYWVcf2Amrqs4cbjaW6n95e3oAT9oGsI0EiXZ0jEZwXy0MaamI5D4Fy+n96rrCS5YfMSezvaM4R459fvo0jS6bnw3PVfdgKOnm9pf3YFjcCDtdQxdp+Pm9xLPKwCsgvq1v7oD+0B/wjPt7O5EmAYdN78X38pV5OzZOWmv9ahdhS+/gOlw0PW291CweWPGsdJmR2pTqLcsJSIatWKclXdDMTFpHRlCiCeAbwkhCkbGXQl8+UQZqTg+ePbtpuzxBy3P7Rjvbd0vfkzTRz9H8cYnyWpuIJZXQO/l1+A769xZtzE8rwp3W2rMM6Q+L7R4nIJXXjwmsawH/GQ1HsJ0uRleuCTjHJr3xmtpl071aJSiF55WYvkUY6aqYUz4kzmSPPKJmbiW4sQjohHm3/Ed9GAwabLKtP4q7XakKdEyxC+DlUw3kdiO5hfQf9Gb8Z5/UcKbnHS8w5E2SU4CsfyCI+NcbsI1dXS84xZqfvMLRDyW8H4DSE3Hd84aBs+7IHFM4QvP4BjoS/Ig6NEIBVs2M7BuA63v+yiFm5+n6KVnsQ160UMhxIQBKBZCSopeeo6+S6/C01Cf9rORQGDxssmJXikpev4pSp98BD0UxHC66H3z1fS9+Wolmk9TpuvIkFIOCCG+Cbw2cqpvjCb7KeYoUuJqb0WLhAlV16Z2CE1D8bOPo0XTvOQbBvN/+j2EaSKkxD40SNXv/5ferg5633LdcTA+M72XXUX1b++a9OqkFo1YidlTDV2TktLHH6Tk6UetHBEJpt1B862fIVQ7HwwDLRLGdLlB06ySn0b6Z1a6/BjFyY3KGlJMmbwd29JWkUgbb6vpRErKafz4v1C88WmKNz6ZEKijGHYH0mbHlkEsm3Y7He/4R/xnnp3Rpr5LrqDssQfS2tB53TuO2BiNMO9PvyP/9deQSNA0DKeLeG4egcXLGVi3ISWWrmDbK8lLbaPnisfI2f0G4coaBtZfysD6S8E0qf7dXVYljInKJo0gdRtZjYesiT1TV6pwEH04kGiljZTkbXuVkqf/jn1okFBVDd1vvYns+gOUPvlQokSeLRSk9MmH0SNhuq99e+J8esAHQjtyPsUpy7E4MqSUvwZ+fTzsUswszo42an95Oza/z3oxNk26rn8HAxe9OWWsPhyg+JnHyX99C7ah9JUA9TQiUI9GKX3yEfovugxzpKnSbODs7kJqWtpGJOkIVddNK8cjd8c26+UhHoeRZ5EeCTP/p99j4IKLKdz8PMIwRppFXU9g8XJK7I+mlP40dR3fGaunfH3F3EaJZUVGRDRK3o6tOLu7iJRVMHTWuUiHA/tAf3pvxBgkgBAEFyyi5f0fw8jJpfu6tzN47lrm3/kDa1JHgBAMrL+U4uefynie0Lwq/CvPmvB6vVdei+fAHrIb6pO291x1A5ExtTdr7vk5nv17k7zQ0ojTd/HlGRt/yAwZ21IIGB8eoWm0vu+jOK9so+TJv5NzYC96MGCFRmSw3cjKxr9sJZ59u9HGCWYBZDU2sOBHt1H/ldtA0yh54iFKnnksIYpzDu7Dc/A2KwFx3PF6NErxxqfoueJanD2dVP/fr3D0dAOS8Lxq2v7xQ0TK0xY5UCgUJwEiGmXB7d9JSTQuf+jPRIvLCCw/0otGCwVZ9P3/xDY0lGgGlS78LVNInLTZyG48jH/lqknZZhv0UrzxSbLr9xMrKKB/w1usBMIpYPP70lYjElhz8GjDk9FclumGYIydU5OuE4tS9OIziZrP2nCA8ofvp+u6dzC8eDmeg3sTuTqmrmO6s6zVPMUphRLLpyG2oUHyt76MbWiQ4MIl1lvwOEHo6OthwY++hRaLoEciGE4n5Q/+kcOf/QrhympMuz3txDKKAOJOF42f+mJimx7wU/eLH1mTupSAxLA50ENBokUlOHu7U84jdRvt73rf0cMINI3Gz3wZV0sTBa9txnQ66bvkCoyc3MQQe39filCGMY0/MojlUGUNrvbW1IeHpjG0+ry0x0Qqqmh73z+P3ISk+tc/I2/X9pROVtJuZ3jRUqLFJSz6wTcRic9mzGWQOHu7KX/wT/S85XpKn3o0NTt85DrpkEKQ1XSY2l/djh454gVxtzax8Mf/zYF//7byMisUJyl5O7elLdGpR6OUPPX3JLFcuPl5dL8vqWvq+CRsU9MRZoaupVJiuN2TssvR08WiH/4XIhpBMwxkWzM5+/fSde3b6d9wxaTvL7B0BfnbXk6auwBM3YZ/xZk4e7ux+YYI1i6g+61vm3ZjEptvMO32dCuEejRK2WMPsu8bP6Tw5ecp2rQRLRLGd8Zqeq+4lnhu3rRsUMxdlFg+zcjZs5Oau38G0kSLxzFeeYFoYTENn/2KFYsFlri75+fYhv0J4aZHImjRKDW/vYvDn/0KUrchiU7cNGRcE43iZx7D5vcnTdR6LEr+tlfpuvoGyh97MKmahmmzEaxdQGQKdSvDNXV01tSl3efs6bJi0dLENicaf4yrZyyiUfLeeC2t5yVaUES0ZBJ1ZYWg7b23YvvFj8lqPgymaX1+ukbjR/8FdJ1YUQkHv/otlnzzS9jSNFARQPELzxAun5fxHjL9X4hYlNxdr6fEhAusUJKCV15UnhCF4iRlopU+x0AfAM6udsof/DM5+3albT09Kpilw4H33AsQRpz87a+mdDY1nS6CdQsnZVfF3+6z4npHriew5qLyR+7Hu2YdZlbWpM7jO3M10cISnL1dCXukpmG4s2h/9/vT5rBMh2DdQuw7tqX9fNKhRSLo0QgDF1/OwMWXH3W8q7WZskf+QlZzA0a2h75L38LAhZeofJKTBCWWTyNENEL1PXcmCVI9EsHZ203Zw/djZHkoevGZRLe7lJjkkVJxtkEv2ri44/GYNhvecdnAeTu2JQnlI4Ot+OeOm95D+cP3W/HB0sR35tm0v/v907rXdESLSzImZBhZ2WnDLbIb6lNDLRgp/9bfO+lEEmm30/jJf8Xd3EhW82FiOXn4z1id9EJhZHsIV1bjOXQgw0lMcg7shUnG7iVsFQJXZ3tS69hRtFgMd2vzlM6nUCjmDuGKSqTNhhiXVyGFIFhTh6O3m4U/vG3CcplS0+i97Cq6R/I7tHAIV2e7VfXHsJwIUtNp+ufPTFrceQ7sTSs8pa6TffjAhDkoSeg2Gj77ZUoff4j8115GmAa+M1bT/dabZkwoA/RcfQM5e3clxSCbuo4wTNKlr0tdx3BNzsvubm5gwR3fRUQtB5MtFKT8wT/i6mil4+b3ztAdKI4nSiyfRngO7CWd/1GLxyna/DxIq/PchAgNm39owm4jht1OtLQ8JWs6Y7tmoSHtdrwXXoJ37XrsviEMt/uIp3uGiJaUMTx/MdkNB5M8JobDQe/lb00reqUYWysjxfAp2xCqnW9lVmeg94q3kn34YHrvj5SIWIx4bh5aX8/kr25KwuUVZDXUo41bXjVtNiLlFVO4A4VCMVdwNzdQ9YdfW6tiJM9I0man98rrKHniYbRoZOJVQF1PKnVmutwc/vy/k33oAO7WJmJ5+fjOPGdqzT50DTJFc0yxI6npctN147vouvFdUzpuKkTKK2n49BeZ99d7yWo6jGl34F1zIc7uLrIPH0xy9Jh2h5XQnSGfZTwVD/wxxfuvR6MUvPoSvVdcayVtv/EaWiSCf/kZhGoXqEZUcwwllk8j0lV0SDCJyg0ARlYW4cqazKXaNI32m9/H0LlrUyaSgQs3UPbw/WnrEQ+N1u/UdWIFhZOwZHq0fOgTVP3+l+Ts24XUbQjTpO/St9B36ZVpxwcXLiadKJZC4Fu5asYntMCyMxhadY5VcWTcPsPhILBsBV3XvZ1F3/s6WjxzKb4kW202vCPZ3Cn7dJ2BCy6ZEdsVCsXsISIR5v/sBylNO6xymYW0vvdWwlU1eA4dSO/hxZqv0XQ6b7iZSNm4l2YhGF68jOHFy6Zl3+DZa8jf+nLaFa3pnvN4E66uo+EzyeXEtXCI6rvvxHPogOXBj8cYPGcNXWMqDB0Nd0tj2u1St1HyxMMUbN1sJYHHDUqefQzf8lW0vv+jKkRjDqHE8mlEYMkyRJqJa6JmIKP7pBBIm91KtrPZ6LnqBsoeuT8pyc+0Oxg4/yKG1lyY9lz96zeQs+cNshoPo0UjVtiD0Oh4xz8kGoAcb0yXm5YPfwo94MPm8xEtLpmwHqm02Wn9p49Q85ufg2mgGQamw4HhdNPxjn84Lja23/JBshsPYfP7jmR6axpGVjaDa9aRt+1VEFpKjeuM1TYcDub/9PuJklKjj814bh7NH/okjoE+Sp98GAwD3+rzCCxdqbwaCsUcJ2/ndkizEiiAeE4uwYVLYORrh7c/ZZzUdXxnnUfnDTcTz5/5+bfrhpvJbqjH5htEj0QwbXbQBC0f+PhJ1Y3UdLlp/ti/YPcOYPf2Eyktw/DkHv3AsedwutDigdQdUlKw5aWkFwoRjZKzbyf5r21mcO36YzVfMUMosXwaYWR56L7irZQ+82hC5B6ta57UbUSKiomUz2NgzYXk7N1FyRMPEymroO+SKyja/Dx6OIRpd9B3yeX0XHVD5pPpNpo+9nmy6/eTs28XhjuLwXPXEisqmdH7nAyGJ3fSE57/jNUc/PI3Kdz0PI7+XgILFtN15hpwujl62f+pY7rcHPrC1yh/6M/k7toOCIZWnUP39e/EdLoofeKhFO98prbiEit8Qw8daSAzOjae7SF/2ysUvPLiSJy4JH/bqwSWrqDlg59QXg2FYg5j8w0iYulzMGy+ocTXfZdeSeW9d6dUL5I2G+03/xOme3KJdlPFyPZQ/6VvkrtrO1mH64nlFzC4Zt1JWykiVlA47VXP/nUbKHnuifSru5m6AL70nBLLcwgllk8DRCxG1e9/Sd7ObVZymBDEnS6MnFy0UBD7cJo3XkAKje5rbqTv8mtwNx1m/k+/byX2mSbuliaw2Wh5/8cYXrQE0+GcnLgSguElyxlesnxmb/I4Eysqofv6d+DtCbDr5VYiTzaBBE++izMvrMGT55rR68Xz8mn7p4+k3WcfSl/iCMAcV2tZ6jb0cChtAxlnTzeOvh70cZ0JPQf2krtjG76z33Qst6BQKI4jwdoFVuxvNHm1UI7Utx9l6Ow1uFuaKHrxWaSuMVrfvukjnz5uQjlhi83G0NlrGDp7zXG9zlyn9y3X425rwVO/H7BWCqXNxtCqcyh8+cW0x2jjSuUpTixKLJ/iiEiEpd/4N6s02uhGKdEjYbRYDN+Zq8nduzPljVcCvpWr6LvsKgCq/nB3UpawJk2IRam89272f/OHp4UXctgXYduzDRjGETHqGwix5clDXHTDcuyOySV7HCux/ML0y6o2O6F5VbjbW0AIYrn5iFgUu9+X9jxCmohY6jKuHo1Q8OqLSiwrFHOY4MIlhOdV4W5rTkpYNu0Oeq66/shAIei68V30bbiS7EMHMF0uAstWnlShECc70maj+Z8/i6u9haymBmK5eQSWn4m7tYn8ba+mdgG02Rhafe4JslaRDiWWTwGyDh+k6PmnsA96GV68nIHzLyJWWIS7rZn5t38HLZZa5k0AwjTI2b+HaH4BDq83kbBn2mz4Vqyi9UOfBKz2yI6+nrTX1mIRnF0dKS2iT0Wa9vVimKlrZqYh6WgcoHbp7ISTdF99I5X3/y4pu9q02QjWLaTxU/+GFgyixWNkH9xL5R/vyRhmY9psGeuz6sEQBS+/gOFy41+5asK4boVCcQIQgsZPfIHyR/5KwasvocWiDM9fROfb3p22K2c8v4Ch884/AYYqRglX1hAe0zcgWLeQwLKVePbvToTJmDYbcU8u/ZdMvnGL4vijxPIcRIuEyd+ymZz9u4nlFTCwbgPhyuq0Y4s2PkX5I39BxKz6jVnNDZQ8/fdEaZ7xReVTEDC8YBmOLS+NlEkDJPhWH/EqSk0nU/k0IeWky+ec7Pi9wbQfg2GY+L3hWbNjcO06tEiY8kf/BkYcYSbXpDazsjCB7MP1GbssSiHoX38ZxRufRIyr22xqGu62Jlx/bRtJ9JM0f/jTJ13ojEJxqiMdTjpves+0WzwrTjDCSngs2LKJwk3PoUUiDJ11Lv0brlRdVecYSizPMfSAj0Xf/wb6cAA9GsUUGgVbNqgcIk4AACAASURBVNHx9lvwXnBx0lgtOEz5I/cnhVAkkrhG6m4eDWEYFGzdbIVVJLbFqbr31wTrFhArKsHMyiZUXUdWc0NKCaJYTh6R0vLp3u5JhSffzdBAKEUwa7rAkz+zMctHY+DiNzOw7hLsg16MbE/amtSx/HzLezzuhUkCoXnVFL/4bKpQ1nWrvatpwpgM7bqf/5BISRnS7mDggovxnr8e9OTpw+YbouSZR8nZ9Qam00n/+susn9nTIERHoZgJ9ICPglc34ezpIlgzn6Hzzsd0Tjy3ePbvpvTRB3D2dhMtLqXnqhvwr1w1SxYrjhlNw3v+RXjPv+hEW6KYAPUUm2OUPfJXbL6hhEdQkyZaLMq8v/wfWnA4aazn4L4Rr296jlb8S2Il/6XtameaFLy6KfFt2z98GCMrG3OkKL1pt2O4XLR84GOnTZmxuuUlaFqapi6aoHL+7JS+S0K3WS8zGZq3eNeuB5H6Ky7tDhy93WixaEr5OSB9QxTDwN3VQVZrE/P+dh91v/hJUidBm2+IRd/9GoUvPouzvxd3RxsVD9xHzW/uPJY7VChOG7IaD7H0G1+k7LEHKHzlRSoe+CNLvvEl7P19GY/J2/oytb+8g+zmBmzBYbJaGqm5+2fkv5I+aUyhUEwPJZbnGHlvbE1bxF1qOrk7t2P3DiREylS7ICWdb8zX6aSuZhhJ5YeipWUc+I/v0Hn9zQycfzFd17yNA//xXcLVddO24Vjwe0Ns39jIs3/ezYsP7aPlYB8yjcibSTx5Ls6+ZD5Otx1dF2i6IDvXyZorFmF3zr1Fmnh+IS0f+DiG04XhcmE4XZh2O/0XXoJI84IjIG0d7tF9o2ixKFmNh0Y6QloUP/MYenA46WdXj0bJ2bcLd3P6gvwKhWIE06Tm7p+hRyKJlUI9GkEf9lN5790Zj5n313vRxpWR1GJRKh74I6RzgigUimkx957wpzmZfLRaLErlffeAzYZpt9N1/TsZPHctHEUgjq29OzoyWDOfSFk5eTu2ZYxpNZzOlBhV0+Vm4KLLJn0vxwvfQIgtTx3CiFsvDbGowYHtHfi8Ic5Ymz62e5SB7gBN+3uJBGMUlnmoXVaCK2vyWeHFFTlc8rblBP1RNE3gyrbT2TTIrpdbiIYNCkqzWbSqjOzc2Q3LyIR/5Sr23fYTPAf3IeIxAouX4246TNHmjWnHT3aNQItGyN39OoHlZwCQu/uNtC95Ih7Hc2DPhC2+FYrTHVdHG9q4TnwAmpR4Dh9ARCMpSbZ2bz8iTTdUsJK3nX29qV35JolpWCVG062kKU4OtHCIkqcfI/+1zQhp4l+8HO8FFxFcsESFxk0DJZbnGIOrz6Ngy6ZU4WGa1jJALDoSlvEHDJeb1n/6CLV3/yxtu2opBHFPDrG8Amx+H8EFi+i56noi5ZWUPvEQWjR9+2sJxAqK8K065zjc4dQxTUlP6xAdTV6EEAT9kYRQTowxJJ2NXhauLMPtcaQ9T9O+Hup3dGGOlH7ze0M07+9F6AKbTadyYSELzyhDt008kQhheZQB9m9rp61+AMOw7OlqGaS33cfaKxeRU5A+PGK2kXZ7IoYx+8Beau/+GWKi1ueTQdMwxsRSZoqrlLqO6ZobLw4KxVzFCoXLJEwFYkznzVFMlxthpJZ+tM5nYEyjhrJ/MMTeLW0M9llNjIorclixtnpKDgXFiUfE4yz48X/j7O1K5KwUbH2Zgq0vE/fk0PreWxleuvIEW3lyocTyHKP7rTeRs283tmAALRrFFAIhZco0qsWilD36N8LzqtIKZQCE4PDnvpq2Q15oXjWm04keSa7iIIFIcSmHP/uVYwrzOBpSSga6Agz7ImTlOikq96QNDTBNybbnGhjqC6YI5PEIIfD2DqcVy7FIPEkoWzaM/BuXRONxmvf14u0JsOaKRWltGU84GKP1YD/m2HJyEoy4yYHXOzjvsoVHPcdsIiIRan91e0pNz+kghcDR003xk48wuHY9w3ULcbW3pP4cSsnQalWvWaGYiFBVDTKDty9UWZU2L8HI9jC8aAnZ9QfQzCPOFalpBGsXTLlTXmg4yqtPHsIYqb0ugd4OP688fpCLrl9+VCeCYu6Q+8ZWHP29Scndo3OzPeCn7pe3U//FbxAtLj0xBp6EKLE8xzBycqn/8jfJ37KJnP17ELEoWY2HU9obAzj6enAM9GWuo6vbyNm3G304QKygkKGzzkM6LY+of8Uq4rl5iIFYkhdb2h20fvATx7WzUyQUY8tTh4mEYkhTIjSB021jzRWLcLqTPRidTV4Ge4aTBekEZGoMMtAznHb7WExT4veGGegepqj8SNmezsYBOluGcDht1CwpIrfQ+my8PQGEJiCNbQPdAbpbh4hG4hSUZM94h7/pkLvnDSYfaJGMTPwrECPf5e/aTu6+XZQ9+Uj6lQ3Ae+75J217W4VixpASYRiZHRC6jfb3fIDq3/0vxGNoUmLqutWS+l3vz3jatn/8CPNv/w72IS8YJuga8Zw8Wt/30Smb2Hyg1wq/GEc8ZtLZ5KVqUdGUz3ksSCkn5bRQpJKzd+fETpF4nKIXnlElB6eAEstzENPlZuDiyxm4+HLcLY3Mv/27accZ7iz0CVpiarEo5Q/+yfJQOxxU/PVeGj/5r4SrakHXOfzZL1P5x9+Rs+cNhJREyipof+c/ZazpPFPs3NRCKBA5Em5tSoKBKDs2NbPm8jFtWvuD7Hml9Whh2Qk0TVBUkWOd0pS0Heqn9WA/sWgcm0NP8ipnwoibDPZaYjkSjrH57weJho+8nbcfHqBqUSEr1lRhs2euRCJN2LW5BaREAiWVuaxaV3tCYwD1YDClVNxkScS9awIkiRes0UY26T5ZAWS1qOQ+xWmMaVL8zGOUPPs4eihIPCeXrrfexGCaMmG+s87lcHEpRRufwNndRahuAX0briRWWJzx9PHcPOq//F9k1+/H2dNJtKScwJLl04pJHewJItNMD0bcZLAvOCtiWUpJ84E+Gvf0EA3HcWXZWbSqnMqFhcf92qcShseDHFmVTodmmji7O2bZqpMbJZbnOKHqOqKlZTg725M8wIbDwdDZayh4bTPEU+NPR39FRt8uR/+tvet/OPD171kxp55cWj70CUQ8jjDiR63nORNEw3G8vcOpAljCUG+QSDiG02XHMEy2PtMwKaGs6QJN0zj3sgVomkBKyY4Xm+jt8CUm/0hocpnhQkA8GkdKyfbnGpOE8ihthwYory2wQkcmEL9jw0Z623007+9l/gpr2Wu0csdsek4Ci5eRTtZKIZAIpNOZVEownWUiU8hPBvRQatKSQnG6UP7Qnyl66blExQq7b4jKv/wfIh7Duz41WTpcWU37P3x4ahfRNIaXrmB46YpjsjUrx8FQfzDN6QVZGfJAZpr6nV207OtL5ICEgzH2vtaGYZjULMn80qBIZuD8iyl88TmETF/dSALBmgWza9RJjhLLcx0haPzY56m+5xdkNxxE6jaQkp6rrqf/ojdbYnkciXq5aU6nh0O4W5sI1R75RZE224zFJ0spGegeJjwcJafAlQhZMOImXS2DDPZmDocQmiAeNXG6LHE5qVJwAuavLGX+ilJ0XcM0TA7u6KSnzTdN+6H10AA9bT6CgfSZ5gCt9X0Ulddx5gXVvP5801HPaxqSlgN9lFXnsX9rO32dfkBQUpXL8vMqZyWBJlpWweDqN5G/Y2uizbUUAtPp5PCnv4weDmE6nJgOB0u+/R9gpp9oJ4vUNPwrVHMExemJFgpS9OKzidWXxPZolPK//w3vhRsm5QHWImFsg17iefkZa6rPBHXLS+huHUpZgROamBXPbjxm0LyvN+X6piE5tKOL6kVFEzonZuL6fZ1+pCkpqsjBMQfLgU6WyLwqfCtXkbfr9YzOjYH1l86qTSc7J+9Pw2mE4cmh6eOfx9neih6NEKquQ9rtZB/chzDiifJwo1NMuGwerp7O9GXlhEALH5/WzKHhKK89fZho2PLMSlOCENjsGvGYgRBiwlAITROJ5LzRcxwVaVW1eO3pw0hTEo8ahIYzi9zJYMTNCYUyQNAXQZqS/k4/Qhy1gh8A0UiMVx6vJxYdFaGSnrYhhvqGWX/dsgnDOmaK9ls+SHDBIoqffxo9OExgyQp6rr4hJdEjVFmFu60laRlPCoHpcKBFImkn4LHLflLTMFxuet5y7fG8HYVizuLs6bKcEGlW/rRoFH04gJGTm/kEhkHFA3+k8OXnkZqGME28a9bR8fZbUrpnzgS5hVmsXFvN3i1tiTlN0wWrL6pLySU5HgT9ETQt/TPCMEwiI2EZUyUeN+ho8NLX4cfptlO9uDDhxBmlo9HLnldbEyt9UkqWnD2P2qUnrzc7sHQlebteT7tP6jrxvPxZtujkRonlkwDPvt1U/vEebP4hkJLhRUtpe88Hqf7dXSnZrhLAphOqqiGrtTnlXMIwCNUd+/KLlJKgPwIIsnIcCCF4fWOjJVRl0kBiEUscygkacGu6YOk58xIxvfnFk08w7G3zTTqueabwD4V57ZnDBP2RSV/bNMAcXxJQWnWiOxq9Ey4zxmMGpiGxO/VjC93QNLwXbrC8WhPQ+r6PsvDH30JEo+jRCIbDiXQ4aL3lQ9T89ueImJUYmrSKMRKfLXUb3jddSO9V1xPPV7GGitOTWF4BIp4h/EuA6Z7YS1z+0J8oeOWFRJMSgIItm0FKOt71vpk0NcG8+QWU1+Qx2BdE0wR5RVnH1Zs7FqfbntmZIjMnb09ENBLnlcfqiYZjGIYEAR2NAyw9Z15ivh32RdjzauvItY9c/+DrHeQVuckvzp709YKBCP1dAXRdo7Qqd1YcIJmIFxZh2h1piwPE8k5Ax9mTHCWW5zjulkZqf3VHUpcmT/1+Fv3ov9CCqfFlAnB2ddL8kU9R++ufQszKrAYwHQ663nrTMccme3sC7NzUQjRiPQgcLhtLVlcw7I+kz/SaAJtDJzvHyaJVZRSW59DVPMhAdwCH20ZeURaD6eKbx3G8hLLQSJvwYl3USkDU9GN/kJiGpK/TR35xNq31/URCMYrn5TBvfgFG3GTXy60MdAcAcGXZWbm2iqLynGO+7kRES8rY/7Xvkvf6azi7O4mUVTB09puQDicHv/xfFG98ipzdb+Ds701KGhSAqesElp9BrEAJZcXpSzy/gOGFS8g+tD8p38S02/GuWYe0ZfaSimiEok3Pp+3OV7BlM1033HzcQjI0XaOwzHP0gTOM022noCybge5A0ryr6YKSylz8g2E8ec4pCdBDO7sIh6JHziet+fbAtg7Ka/JxuGy0Heq3VkHHMRo6dzSxHA3HObC9g84mb+JZpOmCPa/CWevrKK2aYPXgOOJfthLT6UKLRZNWAg2Hg77LrjohNp3MKLE8xyl54uGULk3CNNGGA0le5WQkoZr5HPqXf6f0iYfJam4gWlBE7xXXEFh+5jHZExqOsu25xqTktfBwjN2vtDJVh6emCy68egluj4NY1ODFh/YRDsYSglvTBDkFbnwDM5skNtmwCSEES88t5+AbXWk9HqYhJ1VhYzL0tfvp7wxYJfIkDHT7adzbA1ISDsUTn0koEGX7xkbWXrkoZSlxppEOJ4Nr16dsj+cX0nXju3D09+Lq7U7Zr0cjFD/7uNXUZoKYTGd3J1mNh4h7cvAvP+O4LC0rFDNFzp4dlD7xMI7+XsLl8+i5+kaGFy2d8JiW93+U2l/eTlZLE1LXEfE4vhVnHbVkl803hMwwoUpdxz44QKS8ctr3cjwIDUdpO9RP0B8lvySLygWFU/asnrWulu3PN+EbsDzbhmGi6xq9bT76O/2YpqRmSTFLzq6Y1ApbV/NgWoeH0AS9HT4qFxRaJUwzTOOR0MTNm4y4ycuPH0x6bgGJ58KOl5q4+MblOF0noKmLbqPxk//K/Dt/YIVeChBxA++adSpeeRqop9Mcx93RmjY+VIvH08eNAuHySoxsD0a2h9b3T73e5kS0HOzDTFN+TEqmLBwdThuubGsS2f5cA+Hh5InJNKUllMcGZI/B6daJhCafhGZ36ixYWUrtshJ2bW5Jm8wylpLKXGqXlRIJGzTu6Zn0daaDlCDH2GLEJYYx8nmMM9E0JA27e1h9cd1xteloSLs9qZ36WLKaGzjj87fifdOFdN74LsysMd4Zw6D6np+Tu3enJQiEhrTZaPz4FwhX1cyW+QrFpCnYtJF5f7sv4en1HDpA1s9/RMt7b8U/QadTMyubxk9/CUdPF46BPiKlFcQKj16CLZ6bl7HslzANYnMsvKmv088bzzdiSpCmlYvRsLuH869ajDt78pU07E4ba69cRGAoTCgQ5dCOLvxDIaRJotZ+68E+HC5borLQsVJUnkNPmy+l6ZWmHylFmomOxgGrYlKmx4iErqZBapelNgabDSIVlez/+vfJaqjHFvATrFtIPF+FYEwH1ZJnjhMpKZvyMf2XXH4cLLHwe8Np39SlKcnKcUw6LEFosHJtFUIIIuEYg32pISVHTp5+czRiHPV6mi6oWlTI5e86g0vfvpK65aUIIVh5fjVlNRMnOGTlWJN8WXXejIRbTJnkELokfN4TX5LNu2YdpsOZdp/AWgHJf20zC3/8LRizDF3yzGPk7t2FFouhR6PokTC24QDz7/xB0jiFYi4g4nEqHvpT2pCIyvv/b1LLVNHScgLLzpiUUAZrVad/3QYMe7LQNO0O6/fuOFbFmCqmKdn5UjOGIRPhDKYhiUbi7NvSNq1zevJcuD0OAr7U541hSGvVbRKU1+Yj0qgcaUpK5uUmxjhctuRxAmx2neqj1Jbu7wpM6HAxTUl/l5+dm5rZuamZpn09+Gd77tY0gouW4lt9XlqhLOIxtODw8YtnPEVQnuW5gGliHxrEcLlSOuf1Xnkd2Q2HkibqiX6kTYfzuC3PdTZ5Gejyp92naYKKunzsDhv7tx292HlOgTvx1j7dMm8Ca6Lrah5MTFij8W2ePBdSSsqq88ktTH2wRMNxPLlOnFk2IsHUcBbdJsjyWEIwryiLivkFdDUNJrwPQojJVes4TsxW3dOJCCxZQWDJcnJ3v5GxPJFmmtgHB8jds8MKywCKXnwmRXgAiFgMz8G9xxwqpDiCEOIq4CeADvxSSvntcft/BIyuyWYBpVLK/JF9BrBrZF+LlPL62bF6buHs7sg46erDAWy+oeNSWaDr+ncCkqJNz49UmjEZOH89nW9794xf61gY6svQYVWSKMU2nSTBUCCasTpGLGJM6ryLVpXT1+4jGo4nEvw0TbDk7Hk4XJb80W0a51+1mPo3uqywDSkprcpjydkV2I9SPi6Spg7/eKzPwPq6s2kQRCcFJdmcs2H+CU0A1MIh5v35d+S9vhWQxHPz6Xj7LfjPPPuE2TSXUWL5BJO3ZTPzHrjPqntrmgSWr8R73gUUvvwC9qFBAouX0XXtTZQ9/pDVUtg0iJSUEc/Jw1O/L6Ujm+lyE6qunXE7A0Nhdk/QTU/TBdWLi+nv9KPbtJQlrfEMD0UY7B22vA+vtU/PKCFY8aYqapcWj0xylnjOK5o4lne0TJAcWTJMf2pBee2RB+DKNVWUVefRdmgAM26SV5xFw57uzAmAkKjsIcl8nQlvb8TTMf4ami5YcMbUVxxmEhGPU3vXT8hqqD/qWD0Swd10OCGWMzYqkRJbIDCTZp7WCCF04KfAFUAb8JoQ4iEp5d7RMVLKz40Z/ylg7JMyJKVcPVv2zlUMdxYiU81xKTGd6VdXjhldp+tt76H7mpuwDw0Sz8ublcZRU0VaFULT7xv5M511OU+eK6PX1pVlR0pJd/MQfm8Id46D8pr8FPHpcNq48NqldDR46WkdIhKOY8QMupq8OJy65XkWAofTxsq1VaxcWzUlG4O+CVpKj5DyjJDg7R1m75Y2Vq2b+Wf1pJCSujt/iLutGW2kCZXD20/NPb+g6SOfPuYGN6ciSiyfIBzdndT85k5cHW1JE4lnz05y9uxMxKs5eruRdgeHPvsVNNPAcLqIFZdi8w2x8Ee3oQ/70SMRDIcDhEbzhz85rVan6RjqH6blYD+mITHiRnrvAdZEeeaFtVOqxWkYJj3tPloO9E1bSFYvLkK3aeQWZk062S0aiY8pE5R6Tk3TsNl1zr6kLmniFUJQMi83sXQHlqc96M9cj7l6SRHDQxGCgQhOtx13tp3BviCRUAzTkGi6ZoVjC5hXV0B7w0AiwU/TBQ6XjVXratjzShuh4ShCCISAZedWTjlbPR4z8HtD2J02PHnWA9c0TOIxc1rl6IqefZzshoNJZa0yYdodSct/wboFeOr3p4wTpsnw/IVTskMxIWuAQ1LKBgAhxH3ADcDeDOPfA3xtlmw7aYgVFhMur8TV1oI2RvmYuk5g2crjHhIhnU6ipSf25Xgi8ouzMjpRCkuzE06DqeL2OCiuzKGvw580X2u6oHZZCS8+tJ9Y1MCIm+g2wYHtnay5YiE5+cn/HzabTmlVHod2dhGPGUgTQsMx/N42vN0BVqytnpZ9APHoNMPGJHS3DGGsNdFtsx8N625pxNXRmlIkQItFKfv7X2lQYjkFJZZPALrfx6If3YYWCqa8cWvjZh3NMJBmmIpH7qf5I59JbI/n5nHwq98id9d23C1NRItKGDxnLWbWzFRI2LW5hY5G76TGSgl7t7RSXLGc4nm5SHn0ODVd1xj2Tb3U3JGLWiWOpJRTEnq9bb6R8akXtjtsnLNhPrmF7kmdc/l5VWzfmL4lt82h01rfnxC/QX+UIV2wck0V8xYUEo8ZeHuGEQIKyzxoukbVokJa6weSSsfZ7Drrr1vGsC9CPGaQk+9C06c2uR7e3U3D7m6rFbgpcWbbyS3IoqdtCCnBbtdZvLqcqqPE542laPPGtEI5nRdJCkFg/mLKHr4fZ283oYoqspoaEGNKGpl2B4OrzyNWPDNJOwoAKoHWMd+3AWvTDRRC1ALzgWfHbHYJIbYCceDbUsoHMhx7K3ArQG7B3BV1x0LLBz/Ogv/5NnowaDWC0m3E8gtou+UDJ9q0aREOxkYqV0TIL8lOzDWZkKak+UAfLQf6iMUMCkqzWbK6Ak+eNR+tPL+K3S+3Ik1pNTPRBJqusWLN1Dy141m1rpb9W9utZ5EE3a6x+KwKuluHCIeOVKAw4hIweOOFJtZftyxl/j68s4t41Eiaqw3DpL3RS+3yUrJzp7c6MFHvgEyJ6WP3x2PGCRHLro7Mz2hX99HDKE9HlFieTaTEc2AP5Q/+CS0cmvTSlJASz4FUZ5C02Rg6ew1DZ6+ZUTP7uwKTFsqjxGMm3a1DVNQVsGBlGYd2dk04XkqJ02XL6K0ejxDWX6NeaCmheX8vNrvGgpWTf0AbhjlhrPHRQjjGUlThwZPvJjCSrZ2wVRPYbBrhcV4H05Ds29pOea21XFhSmVx/0+qglf76R5vMjbh1X+MfeJ1NXhp3dyeVuQv6ogR9Rzzi0UicfVvb0XTBvPmTy7LXIumXHyWAEJh2h/X/pWn0XHkti/7nv8Ew0AwDw+7A1HUiVYtwdbRhZGXTt+EK+i8+fompiqPybuB+KeXYH9paKWW7EGIB8KwQYpeU8vD4A6WUdwF3AVTULDsls4RihcUc/OI3KH3yYZx9vfiXrcR7wSUztoo3m/R3BXj9+UakKTFNSXfrEId3dXP+WxYnOqiOZ+emZnrafYk5pLfNx0BXgPOvWownz0VFbQGePBfNB/oIjQjwmiXFx9z5T9c1Vq6tZvl5lYlVMMMw2be1Pa0QjQTjDPsiidWzUXo7Mjeu6uv0T0ssx2MGriw7oUCq00AIyC/JxtsznPF4m01PxE2PPWdvuw+bTadongftOP18RQuLSJv5iGpYkgkllmcLKan6/S/J3bkdLZq+XfCEh89iDdqG3RML3XQYcdNqSgKJZiXp0HSBEIKzL64jHjfpHJM0l4mcAheBoUhKuIZpSBr39DB/RemkvcvFFTkcSN8FnNLqvEmd48gxgje9eQF7trQlkhSdbqtBy66XW9MeIyX4B8NTEuXpMOImA90BopE4HY0DDHRZk7In38XKtVWJQvoNu3usxJajYBqS+h1dkxbLgWUryX39tZSVEGx2Dn3uK9gHvZh2B8PzF7L86/9qxeSPoMeiyBiYNht7v/uzSd6xYhq0A2PXmKtGtqXj3cAnxm6QUraP/NsghNiIFc+cIpZPB7Ia6qn7xY9AgjDi5OzbTe7eXTR/8OMnVX1waUp2vNSUNOeahiRqxtmzpY3zLkvt7hoYCicJ5VGMuEn9G52cfcl8AHLy3ZxxDCENE6HpGo6RFbUJS5RqpH2eZFqNE0Kg26b2NDZNyYHtHbQd6s84xpXt4MwLq3nlsXpi4zzalj0ipVb0vtfaaDl45JxCwBnn1zBvwcyL1+HFyzE8HkQ0mhxa5HDQ85brZvx6pwInz2/5yYSUZNfvJ7t+L66uTrRQENPpJOfA3knFeI7H1HS8b7pgxs00TUlXs5e+zgBuj52qBUVWg5DI1OOwdJtGdo71du7tyZykVViazeqL56PbNExTkuVxMOyLpPUwa7rAne1g8eoKdr7UTDzNGCNuYsTNSWcVZ+U4qV5SRNuhgSOVLTSB3aGz6MypLyHbnTZWX1SHYVh22B2W5yMjUh7zsltns5c9r7Qhpcn4vKPAYJitTzdwwTWLyc51EQ5mjqkeT3g4Numwlu633kTO3l3IaCSRZGo6HHjPvYBI2TxcnR1kNdSTXb8PEU1T+QKrE6WIRZH2E1/Z4xTlNWCxEGI+lkh+N3DL+EFCiGVAAfDymG0FQFBKGRFCFAPrgO/OitVzDBGLUfeLH6OHw0nbPQf2UPzs4/Rdce0JsmzqDA0E04tNCQNdVtOP8THGE3lHBybYd7xwOG24PY60yXUCyMlPTYKsWlTI4V3dKfc+WvliKtS/0Unbof7Uz3Gk0oYn38Xqi+pwZztYd90yWuv76WgYIDySq5Kd42TxWeVJpUvbDvcnCWXLNtj1cgu5xW48uTOc2KlpNHzyG6wD7AAAIABJREFUi9T+8nacPV1WwxzDoPvK6xg69/yZvdYpghLLM4yIRFhwx3dxdrYnymMdLXRpIiRgulx0X/v2mTIRAL83xKtPHkp6C2/Y1cPCM0tTloYmg27TKBvxzLqzHfi94ZQxmi4oLM9JiEVNE6y5chGHdnXR0eDFNCXZuU7MuAnCKkVXs6SYeMzM6E3Q7fqUxefSc+ZRWOah+UAfsUic4nm51C0rmdZ9J+zQNfQR74XNplNU5qGvy5/yH+9026cdHwcw7Auz++X0CYqjGIZJw54ezryghpx8N97eyT3QHFNI9IsWl1L/b/9J6RMP4Tm4DyM7m75LrmB4wRKWfuPf0CJh9EgE025HTPCCmF2/n8CKVZO6pmJqSCnjQohPAk9glY77tZRyjxDiG8BWKeVDI0PfDdwnk+OTlgO/EEKYWPX4vz22isbpRM7enWlr0GqxGEUvPXdSiWVpyoylKSSM3GfygCMJwGnyPE5Q6bOVa6rY9lxDIicErOfL8jdVpvUi1y0rob/Tz1B/CCNuWi8EAs684P+z997hcV3nnf/n3DsNA2CAQe+dnRIpibQqqV4s27JlZ2M7zsZeJ3FJvKnrZFPtTeISxz/HluMkG3udrOO4t5UtxWq2JFosoihSbGIBQfSOwRRMv/ee3x8DDDGYGQCDGYAgeT/Pw0fCrYfE3DPf+573/b4t2Jawh5uPrhuJxlyZCsRF4vusbF6xuc1uoXN7La2bqpjxRbDZLThL0+f/7teyr+aePTLMTXenR/zzJV5RSfcffRzb+CiW4AyRhqZ16bayXjDFcoGp+8n3MlaZLiZBDEVJNnHIRLBjA1Ip3KRkGDJNKM9x4WTunepK3UXs3NOanKTattQwORJIm1CEEGlLSharyuYbG9l8Y3ZvaItVzVgVraoKHduWn4Ixfxw1TWU5RxRyYdstzRx6+jzx6Fy1toKiCHbe2ZbzeOczcG5qWe4h/qlEk5euHXWJL5UlUjFUVdCWY0eseGUVQ7/y/pRtHV/4FGrAn0zPUOLxRV8UG77zNSbvfRjfzl3opa5FjjRZCVLKJ4EnF2z7ywU/fzzDefsB0/AaUMOhDP5fs/si6UGB9UxZpTPrd1F5lTOj0KxucGW0hlNUQcumqsIOcJlU1JZwy4Mb6Dk1jt8Txumy0bG1FndNccbjFVVh172deMaCeMYCWG0W6tvKc86pji3iq6yoArHgX1fKRLfVnlOJAmvDkJSUO7hhbzsO56V7L7aauxx7unyI1dSx5PqjYaBEIwkxfQXm6RcCUywXGPfL+9OE8mIYFivj97+JWEUVjd/9d9RY+oNRevY0W/7sdxl7+G1M3f1g3mPsfX08e55wjiFwRRXc8tCGlKU7d00xm25q4OyR4YQDw2x19I49bdgdKyv4uP62Vk69PMhYnzdpRN+2pZq2LZenjehSOJxW9rxlM+ODfgLeMM4SO7WtZVgs+b30hIOxZTVacswW6lTUlrBzTxuvvzJEZNZ+rrrJRTQcx+8JJyfw5o1Vef9bqsEZivoupuUxZ/tyFoBt2kPD975O/Q+/xdibHmXy3jfmNQYTk0IT7NiIyPCCKoUg2LnxMoxocXxTIS6cGCPgDVNcaqdje23SalKZLZg7caA/GZUVikBVBdvekDnfWLUo3HhXR9L5Z24BoqapjNbLJJYhEaTZccfyfYqFEFTWlVBZl5vt5nzsDkvWKLs0ZFqB5FCPh55TqQXWfk+YfY+/nkwzbNtSg6KKrMXupe7LGO01DGqe/BFVLz6L0OIYjiLGHnoEz557s5trX6WYYrnAiAydybIhSXjOTjzwZpAS9+H9OHsvJAXz3ILY3DXrnvwhWpkb3435uV8MnM9emJArLRurMvpotmyooqHNzfREEFVVKK9eud8mJCbs629rQdvdSDSi4XBak2kP6xVFVahrLU9pbpIvFbUlGaP2C+mc5xBS3eiiutGFFtdRVCX5ewgHY0TDcYpdDqy2/FcuhBbPOoFma0wwt03oGnWPfxfH0CAj73g3evHKv9BMTApJrKYW741voPzY4WShqhQCw2Zj9JFfusyjS2ViyM+xfb3J+SESjDM9EWTbzU1YbRb6z00Si2iz9pUa0aBGebWTlk3VKZHOhbhrirnr7duYGPYTjyas4xY6TlwLKKpCy6Yq+s5MpHk/17e50+bRnpPj6XO1JCmeA7EIJ/b3L3rPDTfUL7rf0A1G+ryM9nlRLQpNXZVU1pXktYI5R/33/4OKQy8lU0qV4Ax1P/4eQsLUndeWe5EplgtMqGMDxefPLMvtwrDaGHvj25LLGr0f+n1cx1+l8oVnKO7rSUvLUGIxan76//IWy5Fg7kWGmahrLWPDzuwPssWqpjTxKAQWq3pZW4Rebho7K7h4ejzRZjWLXq5vK6e8On05cuG/W1GxjaLiwhXXaa5yNFcZNs9k+k5FRRcCVc++6iKA8lcPUnzhLN1/9HFTMJusG4be/d8It3ZQ+cIzWEJBZro2Mf7wo0RrFxcya4mUktMvD6aJM0OXnDw4gBCXWkfPeCNYbCq3Pbxx2akIqkWhrqXwbb2vNDbsqAOZ8J0WQiClpLGjgs270lMJo+H8vmtbNlQQCsRwFtsztvbWNYOXn+km6IsmC8snh/w0dFawdXd+HtdKKEjFwV+gaKl/B3VWh0ztuSclJcM+OoRtYpxoTR2xdfRcFApTLBeYkUffRcfnP4WIx9JttRYgpEGsep4Dg6riv2E3ajRC0dBAxpQMq9eT9xgtNgUttrhd21LUNLnYcUdb3mMxyQ2LVeWWhzZy9sgQYwO+ZEqGogqQ0LKpko03NOR0TSkl0XA8Yc+UQ7FLGkIw+K730fblx0CLo0iJFAJpsTLwnl/HPj5K7X/+KNmdMuMlpMQy46fyhWcYf/jRlY/FxKSQKAqeO+7Gc8fdl3skWYlFtKw5tdJIbaBhGJJ4VKP7xBjb8mwccq0hhGDjDQ10XldHNBzHVmTJml5X7LJnLHZfLv3dHoYuerHaVHbf34mzJLU4cKB7ihlfJOUFSdclQxc8NHVWLLuzbSbs46NIiwW0dMGvxKKooRn0EhdKKETbl79A0UDfrKuGRrC9i/5f/8iqd7dcS0yxXEDsYyNUPv8M8bJylHgcoWlIqxVp6Fj9/lQ/Q6sV/9YdaGXpb+qRuuxiJ0Vcr5ASlwPvZCivayzXYcGk8DicVnbsaUv+nEin0Cgps+ccdZ8cCXDq4ACxqIaUiQKg629rydqcYCmCm7bS/Qd/Ts2zT+AY7CdS18DkvQ8Tbm0Hw6By33NYA/5Fr6FoGmWvHTHFsolJDiiqklPJiZQwPuAzxfIKUS1KRmeL+WzYWc+xF3uXTJvLipy1R9UNjr7Qy+1v2pSye7hnOuO1DT3RbCYfsRx3VyCy1V8pCoYjce3mr385UauiazCrq4t7ztP4rX9j4H0fXvH91xumWC4QzgvnaP/nz4GmoRhGIqKmWogWF+MYGwFEwgbOZkcYOr4duxh653szXivc2kG0rh7H0GDiAziLYbUx+qa35z3WGV/+FdyFyIcyKQwL0ymmRgP0nBwnGIhSWuag8/raZJOS+fg9YY6+cDFlsvVOBjn09Hn2vnVLzm2154g2NDHwax9M36EoDLz3g7T+788nXiYXuYZpYWRikhtWm4q7uhjP+MyyC7XzqSMxWZrqBhfbb23m7KsjxCJaojhyJbpZQigQJeiPUDzPcznr17AgzZkjV7QyNzMbt1By9nSKDtGtNjy33om0WFBn/JScPZWyHxIBD9eJoyiR8FUTXTbFcoFo/Na/pXQpE1IitDiOsZHZj2xiEUwqgrN/8Vl01yK5vEJw8cP/g8Zv/SuuU68BAr2oiJG3vStvT9rwTAwtnl8KhlAE9QUsWjMpHEM9npS8xWgojmd8hp172tLaa89VaacgIRrRGOqZpnlDZcHHF9ywhfN/+kkqX3iasmNHsHo9aVO6FAJpGDR+/cuo4TCRugY8d9yD5l5ed8HLRZ/HXG0xubxcd1tLumWlKtDjRprbgqIIGjvX9zN1NVDf6qaupZx4TAcpefnZCwR9udvBCUWkWcw1dlYw4xtOm8cVRVDbkr816sB7P0jLv/4jxd3nkBYLQovj27mL0be8g/JXDlD5/NMIPYvtnaKgBmdMsWxyCXUmgG0qQ1ETqQ4AgoQvZ/WzTzD69ncvek3D6WTg/b+NEo2gRCJopa6C+BtOjQYQiliWVy8C2jZV0X9+KmkzpFoU7EVWOq+vy3ssJoXFMCRnXkmfOA09Ufiz921bEEKgaTp9ZyYZH8iSDiHh3LERGtrdeXcbzES8opLRR9/N6KPvxv3S8zR87+spxaxCSpwDvTgHehGA6+Qxap59Eu+uWxj8lfevy/bCCaEs+dhHNie3ffN/Xb7xmBQeocVxnTiaKGKqrce/fce6+yw6nFb2PLKFiSE/M74IzhIbtc1ljPZ7OXVoECkl0pjtuOqy056jt7rJyhBCJOtBKmtLViSWpSEpWWAj19RZwUivl8B0OGkHO+d/XerOX6QajiJ6P/yHWD1TWKeniFXXopW6aPnqlyg5cypjXVVyvKpKvLzwrbovF+vrSb9CkRYLy11bEUD5kYNLiuU5DLsj45K0lJLxQT/9ZyeJRTWqG0pp3Vy9ZGXznHWYvohYttpVGtrctG6ppqjYRmNnJQPdU8QicarqXdS1la9727ZrkaAvgsxSPBeLJop/rDaVA0+eJxRYfLLW4zr7fvw6sbCG1W6hdXMV7VtqMlZk54PQNVAtCCPVcnHhSyZA+ZFD6EXFjPzSewo6hnxJCuUPddK273OXezgmq4BtcpyOL3wKJRpFiUUxbHZ0ZzEXfu9P0daZIFAUQW1zWbKjKkBDewXl1cUM90wTi2pU1ZVS3egq+PNssjTBxebeLO1+FVXQeX1dWiGhoirsvq+T8UEfY/0+LBaFhs4K3BnckPIhXlFJvCKx0lh8/sySQlm32Rh746Pr7mUyH66ev8kaIzSNquf+k8qXnk90tnEUQSiY4oCRzVtWWaT973I5++owg+en0GejiEF/lKELHm59eNOifpnVja6sggpAtQg23dCQsjxXUu5gSwZbHJP1hcWqZv3dSpmYWE+/MrSkUJ47PhpK5KHFIhoXTowR8kfZfmtLQcdc88wTSQ/PpRBSUnHwRUbf8ktI+8pbhheSwekQSaF84Iv0Prm63bZMlodtbATLTIBIY3NBloFb/vUfsQT8SScXNRpBicdo/vqXufiRP8r7+muBs8ROl7kieNkpq3QyPRZMS4sRCjiKbMSiWkrTMHuRhc03NWb161cUQV1L+ZrZ+pWeOIqSRShLIO6uZOyhR/DesmdNxpMPifl7eZhieSVISeuXH6P4wtmk8J372Os2G2oshqGqGXN5Eo1IOvO6fWgmysD5qZTldmlIYjGd7uOjbL8ltRNTJBRjtN+HoRtUN7i47rYWjr/Ul9bBVQiw2iwFbaJhsnYUldgyWxUJcFcXc2J/HxNDgRVd29AlI71eunbU4XAWyJtZSix+X26nCAVLwEfcfvmXjwenQ+jSMIXyOsLqmaL1K49hHx9L2liN3/cwEw8+suKOY1bPJPaxkTTLQ2EYOHvOowZnTE9wk2XTsrGK/rOTaWJZtajc/FAXAU+YiSE/FpuFhnY3xa71ERiYQ1ptiZRQI732Kdi1iYv//Y8vw6hyZ27+3nNfe3Lb1z+e/XhTLK+Aor4enD3nUiLEc6snkYZmQq0dxGrqqHrmCWwLCpikxZJ316epkZnMOyRMDKbmoZ4+PMjAuUsd+7qPj9LQXsGeR7bQf3aSsX4f4VAMRQhqmsvYfFPDquSpmqwNO/e08fIz3WjxhN2QqirYHBZsdgtjA968rq2oAv9UuHBiWQji7kps08vvKCmkRHMteJmTEmfPecoP70eJx/HdsJvA1usLkuOfDVMor0MMg/Z/+AxWz1TCpnN2eq5+7j+Juyvx3nzHii6rRKPIbJ8lRcmpa6uJib3Iyhse6OLUwUH8nkRks6zKyfZbmrE7rNgbrFQts5lXeCbGmVeHmBwKgEi0IN98U8OyG82sBO9NN1P1wtNpTdN0W8Il40ogKZTvbeGDm44nt//WIueYYnkFFPeczxg1FoB12sPo7/8ZAN7dt1L/vW9QfuQgQteINLUw/I73EGlafj/7TCiqyBokiUU1XtvXS01zGTP+SIpQhoQ5/fBFD1UNpWy6sYFNN+bWwMJkfeMstbP3rVsYH/ITCkQpKXPgrinh+e+fYokeOUsiJdiKCjtljD38Nhq/8+9pqRhzQ53/MTesVib33ou0pYr1+h98k4qDLyJisURB4PFXCXVsoPcDvwtqYbo9Llyu06XBxz7cRdv+x0yhvE4o7j6LZSaQ4mcPsx3HnnkC7xtux33gRaqffRKr30ukroGxN7+Dmc3bF71utKYOqVqA9N+zVlyCVra+cpZN1j+l5UXc8tAGtHhCR6ykK20sqnHgp+dSHDLG+r14J4Lc/pZNWRul5Eu0oYnx+99MzTM/Seggw8Cw2gi3tBOrqklEnFcxUFEI5oTyB7peY/oT317WOaZYXgFacUmiqC+DYNaLUxPr45VVxN1ukODffgORhvwN4GsaXZx+Ofv+0X4fo/3Zl7elAf1nJ83WpVcpipraljYSimVOnl8EIUgT13aHhbLKlZvcZ8L7htsRsRh1T/wgkQen68TLKwi1d1E00It9cjwR1VNUJvfey9ib35FyflFfDxUHX0yxbVRjUZw95yh/9RDe3bflPcZMy3VI3RTK6wybZzL9QzuLxeel5skfUfX8U6iznxXnQB+tX/kH+t/7QQLX3ZD9wqrK8DveQ9O3/g0RjyVXEaXVxvAv/9qK0ztMTFYikucYOD+VktsMiY9/PKYzcnGa5g1V+Q4vKxMPvgX/9TdSfuQg9qEBSrrPUjTYR/s/fhbDbqf/fR8m1Llx1e6fD32eYFIoe//220wGl9fozRTLK8C/4yYavv8fadt1m43Jux4AEjZDHX//SewTY8ne6tXPPknZscN0/4+PIa0rXyax2i1sv7WZkwcGVtwZKB5NFG9Fw3HOvzbC+IA/4Z/cXk7XdXV5PcQm6wubw4qiiGV/VhRVUFVfyuRIINl8xuawcNM9Hcmfo+E4hiFxOK15N6iZvuNupm+7E0vAh17kRNou5eiJWAzLjB+ttCzjM1N25BAill4wq8ZiuA/+Im+xnG25Tmoa3V8zhfJ6ItzYMtv6KZ1oTR3VP/9pWnG1Eo9R/8NvEti+c1HR69t1C/FyNzVP/wT7+CiRhkbGH3gL4TzqTzRNZ8YbwWqzrLu8VJP1j2c0kHFO1zUDz1hwVcUyQLS+Ed8Nu+l8/pmUlUE1GqHtf/895/7802iu/L2eC0lCKDfnLJTBFMsrwnAU0febv0vrVx6bDTEYCN0gXl5Bw/e/QeN3/51IXSO2yfGkUAZQtDhWzyTlRw4ynWelaH2rG3d1MS/95OyKmoxUN5URi2rsf/Ic8dlWxwD9Z6eYHA5w2xs3rriDm8n6QlEEndfVcv610fTJVSSCzvN/1zv3tlFVX0poJop/Koy9yEp5tRMhBAFvmOMv9RPyR0Ek8u+uu7UZd02eBU6KknE5W9psxCuyT/pCyqx2S2kVrDkyXygvXK7LZZI1WRsiza2Emlpx9l9E0VI7n3p33ULtTx+HDE5EtmnPJUejRQh1baK3a9OixyyXi6fH6T4+mniJNSTFLjs37G1fcZt5k2sPR7ENRDBt7hMCHMWrl7M8n8rnnwYt/ZkSukH5wX1MPvDmNRnHcujzBNlzXwsf6DyWs1AGUyyvmOCGzbz+15+n9PRx1OAMNc88gW1qAmU2NaNotqHCQtRYDNeJo3mLZQCH04aj2MaMN/f21e1ba+g/N4kW01NWLqUhiQTjjA/6TVeMq4jWzdWoFoXu42NJv+X2bTW0bakmFtHwjM2gqgqV9aXJAk9niR1nyaWIVyQc59BPu9H1SyI0PBPjyM96uO1Nm3CWrk50TAmHqH7up5QfOYgUAu/u25i850EMuwPfzl24D76YXFqfQ7fZco4qJ8Tx/G8euaLlOpPLR9+Hfp/673+D8iOHEEYipWf47e8mVlNH3RM/zHiOVFUM69qJ1NE+L93HEy+ucy+vAW+El5/pZu9bt5jexybLonVTFaN93rQAiFAEzV2F776aCfv4eIpd7hyKFsc+MbYmY8hGekdVuWKhDKZYXhmGQdFAH8LQCWzfSdmxw6ihmaRQhkvBrkytfDVn/nmf0+MznHlleEVCefOuRqw2lckhf5p9DSSWcSZHAqZYvooQQtC8oYrmDVUYuoFQRDJ9wl5kpb5t8SKl6YkgR57rSRHKc+i6pPv4KNfd2lL4piWxKJ2f+2tsnqlktLDmp49T8dLP6f7oxwh1bMB//U24jr+aNMnXbXYiDU05ieVLecmt3LN5NsKoa3QZh02hfAVh2B0M/cr7GXrnr6HE44mGTnOpQzW12IeHUgoADYuF6V23FqwQdDlcOJm5zXw8pjM1Gli2E4LJtY2rwsmWXY28/soQiiKSQa/ttzavWuBiIaH2TooGLqZoH0gEK8KtHWsyhkxk6qiKFsf76Y+ueC43xXKOOC+co+WrX0INhRCGDkIQq6hCjabnL2aSDdJqZfq2u/Iaw/igj6Mv9i63aWDaoNzVCbFuy2IvI0SimMvk6iTX9Jp4VOPIzzIL5TlGer2M9HkpKrbRdX0d9W3leecyA7hf3o91ejplWV0gsQT8bPjUX3Duzz/J4K/+BqWnXsN96BeIeBzfTbfgu2E3AK6jh3GMjRCtqsG/46astQLJdIvOY2j7DiS3e1/oN4XylYhqwVjQPazvN36H9i99BksgYa8pDEmorZORR5fXTbVQRIKZreaklISD+TesMrl2aOqqpK61HM/YDEIIKmpL1tT6derO+6nY/wJS15N6xxAKhs2Bd/etazaO+SzWUbU3j7ncVEQ5YPH7aPunz6HOt7mSEtvUROYo8tx/LXP/zIKJex4i1N614jFIKTn18uDKhDKJ4q3AdARXhZPWTVVMDvszLuPM7+Bncm0z0ufFWEQoJ5GJtIwT+/vpOTnG7vs68/b7dJ04mvq8zSIANRyi+pknGX30XQS270wUac1infbQ8flPoIZDKNEoht1O/Q+/Rc/v/Qmx6tQJc/HqaFMoXy3EKyo59+efpvjCOazTU0Qamok0Ni99YoEpKXfgnUjvHCaEoKTcsebjMbmysVhVapouTyFd3F1Bz+/+Txq//TWKBnoBCG7YwtC73ptY1cmC0DRKXj+B1e8j1NZBpLEwnWFThHKB/e9NsZwD5Yd+gZgX4ZojW32RAAxVZeyNb0WqVvzX7SRelV/nsVhEIx5JH8NyEQjss+2wK2pL6NheS8+JseTyuZSSbTc3rdkyjsn6JDAd5vzxUXwTIQxp5OzRHPRHeeknZ7njLZux5bFKoRWXZG0bL4Cy115h9NF3pe1r+vqXsfh9KLPG+Wo0ihKL0fJv/0T3Rz+ePC7fog+TKwwhCBaoSG+lbNhRz5Gf96QEKYQiKHbZKa8qrDWjiclqE2ls4cIf/DkiFgVFQVoWD5A4hvpp/9JnEZqWbGwy07WJ/t/4yJLnLsZqCmUwxXJO2CfG0gzv58i24CwtVgJbdxAtgL8ygGpR8mouYbGqVNZeci7o3F5LU2cFk8MBhCKobnRhtZm2cdcyfk+Il5++sGjaxXKIx3X6zk6wYUf9iq/huf0uyo4dztgECMg4uarBGZwXLySF8hxCSuyjI1g9U8QrKpOTqymUTVYbvyfM9PgMVptKTXMZO+5o5fVXhoiGE4GP2uYytr6hqSCpSyYml4P5lp9ZMQza/unvUYMzKZqppPsMNf/5OGNveUfWUxcj0TRq9YQymGI5J0KtHZS/vD+jYM4a/dI1tLLCFcpZrCquyiL8U+FlHKtg6BKhCpCzXrl3d6QVYdmLrGbahUmSM0eG8xbKQLL9ej5iOdS5kam991H186fSni/dasNz6960c0Q8BoqADPraUBQmJrz4cDA3ueZT9GFishiGIXntF31MDvtBJiLIpw8PccOdbex96xa0mI5iUVBNm06Ta4Di7rMosWjaXK7E41Tsf37ZYjljR9VVFMpgiuWc8O26hboffw8RDqULYyEwVDXV39NiwX/dDejFeXrQLqB1UxUn9g8seoyiCtq31dDYWYF/KozVbqGsssiMXJgsiXcyPZ9ypVgKsEox+rZ3Ei8ppe6JH4CUCCkxbHbCTa14br8r0bZq3udaK3OjFZdg806nXUspsnH9L+8GVXBPl4UqUyibrCL9ZyZS60Jm3YeOvtDLXW/fitVufgWbXDuooYV2bpdQIksHACFzR9V7Nqir3lG1IE+qEOIh4AuACnxFSvnpBfvfB/wdMDS76R+klF8pxL3XEsPuoPujH6f9i3+LbXoquV2qFkYffhTnUD+u40eQFitC0whs2c7gu99f8HFUNbiSZvaZUC0KJeWOhLeuqlDduDYG5SZXBxaLQjyWHpZVFIFQQNeWnweUd7OSWabuexj/zl2Uv3IQNRREWq2Uv3KQrX/820hFJV7uxn/dTqbuvJ94ZTVD73wvrV/9x5T2xGqRnds++yZatp5CahragQN5VUebmCxF//mprJ0zxwf9NLQvbtloYnI1Ea2uTeQ2Z0CQcC/yzzoZZWJOKH/sw11sUI4mt8d/sW9VhTIUQCwLIVTgS8D9wCBwWAjxuJTy9IJDvy2l/Ei+97vcxCurOPfxv8M+MkjpydeQViuh1nbilTVMud6Ixe/DNjFGrLIarXx1JkKb3ULb1mr6Xp9MWS4XAqobXTR2VlDd4DLN7U1WRFNXBX1nJ9O+5FWrwm1v2sgLP3h92dfyT4eQUhZkRSNeVcPEQ49Q9urLNH3jq8kWq8LQsXsmqXrhOSr3v0C4oQXH6BCGzUa4zI2iadTf1M6N96rYTx5n+mSibbUZUTZZbbR4llzXI6kSAAAgAElEQVR7KbPuMzFZCzRNRwixZilAjqEBOh77dEKoZCi8EoZB83/8Hy66ygh1bkzbP7+jatv+x5h+oT+5by3m8kJElt8AdEspewCEEN8C3gosFMtXJELTcL36MlUvPovFO43mcuHZcy/e3bcRbvHR9M1/xTITACmJ1Dcx8GsfyPiLzpWgP4J3MoTdYaGirhQhEg4F8ZhOcakdi0XB7rQQjWgIIXBXF9O1ow6Xe/GWrSYmS9F5fR2+qTC+yWAiw2G2gclNd3ckC5KWi2d0hu7jo3nlLS+k7sffTQrl+QgkIh7H2XchkSYVhaJolMq7dnLfb1XS+2SUAKbbgMnaUVlXymi/N6NdUkVtYdPzTEyWg98T4tShQQLTibSHiroStt3cTFHx6naxbP7av6BEwlnNECBRb1Lz9E/o/fAfLNpRtftrUdba1rMQYrkRmJ9AOwjcnOG4dwgh9gLngN+XUi6edLsOELEYHV/4FEVD/YjZX5ot4MPxna9Rue857OOjKPFLJvJFg310fv6TnP3YZxb1GFwMw5Acf6mPiSF/MhqnKALFoqDNLo3rmpHiV6daFJylNlMomxQEVVXYdW9HUjDbi6xUN7pQLQqB6XC2wEBGDF3Sd2aSzu21OTdDyYiuYfVMLXrI/MnYosWZ2XeE0yU34qwqzf/+Vxj5pMgJId4L/Pns9r+RUv7fNRn0ItjGR6l7/LuUnD2NtNnw3Hw74w+9dXmV+JeBrh11TA770eKXVgAVVVDbXEZJmempbLK2hGdivPzMhYSGmMUzOsOhp86z55Etq9bQxOKdxjY5vqhQhsTcbR8bSYki37PxkojvMg7T/ZfPr8oYl2Ktqgt+DHxTShkVQnwQ+L/APQsPEkJ8APgAgMt9+ZdIK194FsfwQFIoz6EYBo7hwTTBIGQislX26stMZ6jSXwzvZJDBbg/eiSChmRjSkMypYR1gYQ7pvHvrmsHA+SmaN1RS7DInYJP8EUJQXuVM830tKXdgc1hyijBLKYnHdOxFBZiIFRXDUYS6zGIQAEODmTHfNSeW80mRE0JUAB8DdpGYbY7MnpteNblGWKcm6fr//holGknMybEoVS88S/GF8/T83p+mFHmuF4pL7dz6xo10Hx9lajRhHdeyqYrmDZWXe2gm1yC9r4+nNZiSErS4wUjfNE2dq/O5THQ7Xvo4CXgray91VO16De+nvp3c7wUuV6OoQrxGDAHz2yA1cSlKAYCUckpKOZd9/RXgpkwXklL+i5Ryl5Ryl7OkcHZrK8Ex2E/dEz9I82qdQ0iJkmFtTY1FcQwP5nSvCyfGOPzsBYYueAj6o7NCOTcMXTI+6Mv5PBOTXBBCsOOO1pwiEEIIvJMhJob8KRGNFQ6AyT335tTAUigCi+OaLHJNpshJKWPAXIrccngQeEZK6ZkVyM8AD63SOJdF9bNPJmyn5kUpFE3DMTJI8bl5efSGkbF51OXCWWrn+ttbufsd27jjLZtp2VhluhKZXBa8k6GMq4K6ZuCbXH4AIlfi7krirqU1nbTaOLf3wbSOqvP/XC4KEVk+DGwQQrSTEMnvAn5l/gFCiHop5cjsj48Ay68QugwU9Zyn44ufQWRpQDJHJm9l3WYnUt+w7HuFZqL0nBzL6myRC75leC+bmOSLu6aEO96ymcHuKQLeMFabSkl5EZNDfjzjM8x/bIQiMHSDkwcSxRhSwrabm6hvW3nx6+TdD1Dz7BPLzwUBylurVny/K5h8UuQyndu4WgNdDiXnTic7fs1HiUYpvthNuLmVhu/9B2XHXkEYOpGGJob/y38l1N51GUZrYrL+cJba8HvSdYKiCJylq5izLASD7/l12v75cwhdR9F1DKEgEubjSFVFLyri2Bt/meved2eKUF4v5C2WpZSaEOIjwFMk8uK+KqU8JYT4K+AVKeXjwO8IIR4BNMADvC/f+64WRb0X6PiHv0ssGyyCYbEkTOb1SxEMCUiLBd+Nmb6PMjM+6F/pUNPwjM0UzHnAxGQxHE4rjR0VHH72AtFwHMNIX50XguQqyfyczZMHByh1F604Z9MocqIXObEs4tmZGACoVgtdD16PYjG7UmZhWSlyi7FW6XNaqQv75HjadsNqQysuoeOxTyfqSGa7PRYNDdD+pb+j9zd/h+Cmbas2LhOTKwWLNfM8aEhJY8fqNiYLdW6k+4//isrnn8ExPECkoZmpu+5HdzhQolG6cbDnvtZ1KZShQDnLUsongScXbPvLef//J8CfFOJeq039j76NomdfwpOAVBR8N+zGe+PNNH3z31AjITAk0ZpaBt77IQxHDoV2+QeUk8SjOtKY7dhnYrLKHH3hIuFguivFHNkCv4YhGTg3yZbdubWAn9+1Sd3zEBt/9mMsGVwxABDgaqqg877rClNYeGWyrBS5eT9+BfjMvHPvWnDu85luIqX8F+BfAOpbNhdwRktl8q4HcAwNoC70aRUCraQUm2cyKZSTu+Jx2v/pc4Rb2ul/74eIV16TKwwmJkgpGe3zZtwnBMRjOjbH6paxxapqGPml9wDz5vM46EJlz30tfKDz2LoUymB28EujqL93yWMufuSPCHVsACE4878+i21yAmm1Enfn/mZW0+Ti/GsjSx+4DKw21fRWNlkTgv4IM/7Iyk6WEA7Glz5uHmldm+55L1qtguVr3814vBCC6s2NKUI5OO7H0zOGNMDdXk1JXdnVvgqTT4rcU8AnhRBz+TIPcJkDHv4dNzHVf5GqF55FqgpzSXB9v/4RnP0XU5yJ5hAAUuLo76XzC5/izMf+FlTza8/k2kMaMmWFbz6qqhAOxih2rY2rTJ8nCMh5XfiMdS2UwRTLaRgOB0pwJut+qShE6hsvVV4rCrGalf9ynaV22rfWcPH0eFoTiJaNFYwPBYiG4kumZ861t77Kv/xN1gmxiI6iCPQV5to7S3OblOeqoz+46Xhy2ytqH93zLBTnI4GLPz9FcU0ZnfdvZ/iVi0ycGUbOVoJPnRumvLWatru2XLXPTD4pclJKjxDir0kIboC/klJ61vwvMR8hGHvkvzC19z5Kzp/BsNsJbN6OtNmwBnwYVitqNHMXL0UayEgY14lj+HfuWuOBm5hcfhRVwV6U2cnI0OWaWRnOCeWv/3YASMznUtPWtVAGUyynMXXHPVQ/9yRKtmpqRS24p2fHthp6X59g4bf+0AUv7tpioiEtbd98hAJtm6tp21Jd0HGZmGSj1O3Iqyg1mENUus8zk6yOnv7EpQm1/7vHsz8WhsQwJDNjPvr2ncXXP5kUygCGZuDtm8TXP3VVF//lkyInpfwq8NVVHeAK0MrdeHffmrLNd/2N1H//PzIWXc+hxGPYJ8ZWfXwmJuuVDTvrOf3yYEpgTlEFNU1lOJyr7xg0J5Q/9qFOpj/x0QXieP0KZSiMddxVxcQDbyaw+TqkEGnfw4bFinfXrUhL4d4xDENy7thIRkstXTeYHA4gM4SVrXaVDTvqeMP9ndzzX7azYWf9VRshM1l/WKwqndvrVnx+porsTPR5gmk2QnOotqWL9qRuMN07gZHh+TI0nclzhUmBMrm8SJudix/5Y+LlFUiRydQzUQgYrVn5Z9bE5EqnsaOCLbsasTksCEWgqILmrkquu7V56ZPzZL5QbjvwxXUdRc7EtR1ZlhLXiaNU7nsONRjEv30HU3vvo/83/zvO82do+dq/oIaCSFVFGAbBji6G3/Hugt3eMCRHft6DZyx72kc24lGdtq3VKIr5vmNyeejYXoPVrnLmyHDO3uD2oqWjGH2e4KJFH9VbGxk82J1RCKewyNiknqfvs8m6IdLYzNmP/x3ug/to+O7XU5yKDEVBdzrxb9+R931CgSj95yYJ+qOUVzpp2liJ/dr08Ta5AmnqqqSxswItbqBaFJQ1qHNaKJR7n8ycLrWeuabFcv0Pv4n7wL5kdbV9bJiK/S/S/UcfJ7RhM91/+BdUP/Vj7BOjzGzaytSdDyBtK/ciDPqjjA8kGofUNJfhmwrhmwyt2BHjxR+9ztY3NFHTVLbiMZmYrBQhBC0bE93Ihi9Oc+rgwLKtjyvrShbdPze5Llb0UbWpgcCwF1//ZKIrVY7PkWJRqOi8sqIbJksgBNO37kV3FtPwva+jhsMgDUKtHQz82gfyLu6bGPZz9IWLSS/xqdEAvWcmufnBLrN9tckVgxAC6zJW5gpBwvXiyhbKcA2LZdvEGBX7X0ipoFY0DUIzVD/9Y/zX30Tbl78AhoGiaTj7LlL50vNc+IO/QHPlLk7PvzZC7+sTiZQKCd0nRrE7LHl1NIuGNV77RR+77+2kvLp4xdcxMckHIQSl5Q4UVVn253mhk0ZCHM8nMbl6P70wry31vh33bCM0NUNgyINvcIqZMf+yosWKRaGoogR3R82yxmtyZeHfcRP+627AOj2FYbejl7jyvmY8qnH0+YspL4TSAM3QOXVogJsf2JD3PUxMrnQSzkXzIxdXvlCGa1gsl75+MmMkStF1yl47QvmRQyixSx6uaiyKosWp//5/MPDffiune01PBOl9fSIlqV7qMmf7rEwYuuTCyTFuursj72uZmKyUkvKinGwL56dtJJfoPrL50gFanKpFhPJ8nJUlOCtLqLmumf7955l8fSjrsbZSB7ZiOxVddVRuqLuWPZivfhSFeGXhip6P7+/PunLinQihaToWs/mNyTXMnMVnylyua7Ttf+yKFspwrYllKSk/cpDK55/GNjUJWZuPiIwd/IRhUHbiKANSXrKOWwaD3VNptnCFZMa7Qr9bE5MCoSiCrW9o5OSBgSU/64oqku2uU3LZ9n0u5bjeHAtAhBDUbGnAc34kax5zLBBBtVlwt1WbQtlkUXTNYGp0BmkYlFcXMzW6eG2JyOrDYWJy9ZMUyh/uSp/Lr3ChDNeYWK7//jdwH9qHOhsxzlgxbbEQam6l9MzJzBcxjERrshzEshZb3SKitTISNzFZjPpWN0XFNk7s7ycUyN7Zz9AlJw8O0N83jbXYynUtRRz9x68RGYzjUPN7VooqSihylxCcyN5GPjw1Q8/PTrHx4Z153ctk9RFanLKjhyk9eQy9uATPrXcSaW5d9ftODPl57Rd9iZbtEgzdWDQfv6TMgWoxX75Mrh0ypVvsubflqogiZ+KaEcvWqUkqDryIol1KfVgodxOtrFVKz51G6OmRZQnMbNgMOTpQ1Da7mBoNLJ7PKRJFTy0bqwgGopw/NroshwFFFXRsN4uUTC4f/ukwZ48M450IoloUGjrceMZmCAVi2T/zEvzDiUjd8+enOSCqcaiV/HVXN9W2/NKTuh68nhPfPoART3+G55gZ8xEPRbE6zRfN9YoSCdPx+U9im5pEjUUxhMD98n7GHn4rk/e8cdXuGwnFObavN6fVwO23rb71lonJeuFSR9VW7tlclNioa3QZh+n+2tUnlOEaEsvF3WeQS4hcASixaMbFNENRkDY7I+94T873rmstp/fMBEF/NOsErKoKG3c24KooIhqO0/3aaFrkW1EFNoeFWERDCIGiCDbvaqSidnFnAROT1WLGF+Hlp7uTotiI6Qycm6Ksysn1t7cw0jvN2IAv6R6QjahUiWuCrww28ScdF/Mak6EbqBZ1UbEsDYOp86M4q0oJe4LYiu2UtVaZqRnriKqf/RT7xHgywKFICfEYtU/8CN8NNxN3V6zKfYd6PNmdVRZ0jBQC2rbWUFbhXJWxmJisN5JC+d6Erae270ByX/dVGFGe45oRy4bdsazUiUxHSCBa10jvB38Prdyd870VVeHmBzbQd3aCCyfGMgpmi1Wl1J2wHrIXWdl+SzMnDw4k7m9IhCKoaXRx/R2tREJxdM3AWWpfE49EE5NsdL82mhY9NgyJ3xPGalPxjAaXFMrJ81A4OVNCzBDYlJXl+EspOffkUeKR7GkgiQNh6HBP4v8FKBYV8ZLCpjftpKjCfPlcD7gPH0hZCUwioPTkUTx77l2V+0ZD8azdKZ0lNhxOG4HpMA6nlY7ttdS1lq/KOExM1hspQjlDo6irmWtGLAe2XocwVpYPKYBIQ9OKhLKhG4z2+xjr96JaFbbf0szpw0NoMT15cVVV2Lm3NaUDX32bm4raEsb6fWiaQVV9Ca7Z6EVR8cq9nk1MCsn0xELLtwSGYcx+drNHd7ORTynszJiPeDCW20Uks1Fone6nj7P9nbea3TDXA4skCYvlGnqvAHdNMcMXp9NeAoUiqG0uY+MNDat2bxOT9Uw0EqfcLrjw/V/wxYHXuaW0Ebc1m1FC7mjROLGZCKrNimpVsWRo9iOlxD/owXNhDCEEFV21lDa4V33OvmbEshKLEdi8HdeJV3OuWdZtdnw7d+V8T103OPzMBWZ8keTEO3LRm3qQTBw3NTpDSZkDi/WS9ZC9yErLpqqc72tislbYZ9OCFiIl9J+fXHZUefYsOopC2FcYVYaE20U+aBGN0GSA4ur8fXlN8sN7081UPf90wv9+PhL821avOLO2uYzu46OEg7GUz69qUWjdXDgrOhOTK4mzvR6GjgzTb+jEYmAVHXxHSP6so4cuZziva0vDSNh+nhtJfHnMfgXYXUW07tlMaX357HGSC8+eJDDsSToeTV8cx91eQ+vezasqmK+JBL2Kfc+x+WN/SMnZUzmfa6gq4aZWAttyb5M6eH6KgDe8dKMGmVjOfv4Hp5kaCeR8HxOTy0XblmoUNcMEJVlEKMsFfxLbBPDLdaN5jaeoonjRiOSSCNCjhYuUmKycifseJu6uRJ/tmiqFwLDZGL//TcQrVy+IoKgKNz+4gcb2ikQ7YFVQ3eTi1oc2LKtNu4nJ1cDgdCj5p88TxN/jIR5JCGWAuFSIGCpf7G/Ja8oFGDjUnRDKhkxZFYz6w3Q/9Rrh6cQKprdvIkUoAxiawfTFcQIj3oWXLShXfWTZMdBL/f/77mx0IvOXoCRzrjKAVBR6P/T7OTtgAAz1TOdUUa1rBkdf7OWut29NiTCbmKxX6tvd+KfD9J+bAjHbbGTRj3xip1PRCRkql548gUDy+EQN15WuvMDPWVmKs9pFcNyX6iajJFxwl3KYkYakuMaMKq8HjCIn5//o47hf3n/JOu72uwh1rH6nPJvdwrZbmtl2i+lyYXLt0ecJsue+tuTPoUCE7z7bnfFYb9zKWMxGnX2JOpEsGJrO5JlZoZxpv24weqyP9ru3MnV+NKOHvqEZeLrHcDXkniq7XK56sVy572eIDEUic78WQXahnDhAwer3EqteuyT28UE/De2r90s3MSkUQghKOtx01BfTUu/k5994fakzALlAKCcwEJyeKcYbVym35p7rPEfXg9czsP8cngtjAFiKbDTd3IUWjjF0+ELWhiWKRaFuRyuq7aqfFq8YpM2O54678dxx9+UeionJNcFco6gPbjqe3DYxHuM7mg6kB/EWGMTkjBaJJ/zMsx0gITSZWHGXi4WwV7GOAa4BsWzx+zIWgyw3s0UYOrqzeEX3buxwc+5YJKfosjQk8Zi5DGxy5aBLg3ve1MFvdh7jjm/qxORSqyLZnz4DwSd7OvirrgsrblCiWlXa7txCyx2bMDQd1WZJ5rI5yp2MHe8nOhPFUeZEj2lEfCFsxXbqdrTgbq9Z0T1NTExMrnTmd1Sd/sRHk9stQLVtD8PR9Lm9xKJRZ1tZVBkSwYylFJm9LOHlXNlVx8yINy3goVgU3J2rO3df9WJ5ZvN2SrrPosRTf5mLpV7MYagqMxu3ohevzEqqaUMlI71eAt7w8gWzgIoa07rK5MqgzzMzz0boO9xefiP7vG40udJyCMFIzM53Rmv5tcaRvMamqEqab7KrsQJX4+r485qYmJhcSSTE8XwSQrnq0x9Ns4T7reYB/qanA00KNKlgwUAVkt9uHsiloXEaiqpQs72J0RP9kEEnza34Abjbq5k8O0Jw3JcUzIpFwdVUuerz+lUvlqdvuYOq55/C4vOizFrHGaoKQsmanmFYbSAEsepaBn/1N1Z872gozpbdjQycn2TowvSSxyuqoLrRRam7aMX3NDFZK/o8wTS/zf/aMMJAxMFA1IFmCPSlE53S0KTCC9PuvMWyiYmJiUlmklHkj2y+tFGL03bgi/Rm8E7udIb57MZzPDVVycVwES2OCA9WTVKTZ8dVgIab2gEYOzGA1GejxiIhpJtv30hJbVlik6Kw4aHrme6dxNM9CkJQtbGOspYq0zouXwxHEd1/+JfUPvEDyl47AkLg27kb3w27aP2XL6DE48k0DcNmw7/1egLbdhCtqSPc2rGsRiYLCQaiHHuxl1AgihBi0aIiRRVYrCoWq4K7poSAN8zPv38Kl7uIzuvrKK8yO0OZrD8SBSCJDk7zjemLVIO/6rrA2ZCT3lARJ2ZKOB4oBUDLQTjHVxyZzp1YMMLwkYv4+qcQqkLVxnrqdrSgWMwiWxMTk6uP+ekWbfs+l7Kvd5EufJW2OL9Sn59jUSaEEDTu6qC4xkXPcwnXMjkb3JzuHqOioza5SigUhYqOGio61jZl7qoXywB6qYvhd72P4Xe9L7HBMKh46Xm0UhdWnxcpIe6uYHLvvaBasARnCDuW1/FvIYZu8PLT3fO8ZxdPv3A4rZSUFxEKRBm+6EnabU2OBPCMz3DjXe1U1pXmPA4Tk0KSabluoVCeQwjYXBxic3GIh6qnGIvaOOov5UcTNcxoKvqSjpWSbSUzBR1/NmLBKKe/fxg9riUf1dHj/fgHPWx65EazOck6QQmHKDlzCoEksGkbxgrrSExMrnUGp0MkhfKBLy4qjtcSPa5x8WenL0WWSbhcBEZ9jJ3op35n2+UbHNeIWF5I89e/TOmJo6izhoESsHomqX/8u0ihIAyD2id+iPemWxh613tzEs3jg/6lfZXnEQrECAUyJ8cbuuT0y0PseWRzxv0mJmtBtuU6b4a8tkzU2mM8VD3FbW4vXxls4hW/a1aXZm4u71AMfrV+ZSkYWiTO2Il+pnsnUSwK1ZsbqNpUj1CUZOenmXEf1iIbNqednp+fQi7Ik5O6QXg6iH/QQ1lz5YrGYVI4yg/vp/HbX0PO2ncKQ2f40XczfftdBbuHbyrEcI8HTTOobS6jusGFUMwXJZOri7l21etNKAP4+qcybpe6wfArFwlOBGja3Ymj/PKstl9zYtk+OoTr+NGUgj8BYBip7bB1KHv1EDObtuC78eZlXz8UiKLrK6viz0R4JooW103fZZPLQrblupkX+pcllOfjsuj8QVsfY1Erf3xuI9E01wxJgy3CB5sGqbWnTuJRQ/DsVCUvTZejCsndFdPsrfBgmadntEic0z88jBaOJVOfBg914xuYovXOzZx7/CjRQBhpSISyeHqUoekEhqdNsXyZsY8O0/jt/4sST82LbPjhtwi3tBFpbsv7HudeG6Hv9YlkEfZYv4+yyiJuuqcTxRTMJlcJqyWUDQnHAqUc8JajCMmeci/bSmZyXpjX4zpykZV4X98kgeFptj66G7tr7eu6rjmxXHz+DJlSIzL9XtVYlMp9z+UklkvKHaiqklN0eVGEMCdskzUjsUSXQJeJDnuZJ9eV+47X2uP8WcdFHutvYTI+1xEtkc88HHPwsZ4ubMLggcop3lk/iiEFf9HdxVjUTmw2l3lg2MFBXxn/s/0ic4/H2KkBtEgsRQQbmkFgeJrT30uI6DmWak4iFAWLw+zWdrmp2P88Qs/gua3Fqdz3M4Z+5f15XT/gDacIZUg0h/JNhhm6MEXzhtXrFGhisprMn8shYfH5sQ930bb/sYIK5c/1tnIyWELUUAHJIV8ZN5f5+FDTYE6CubS+fEnDZkPTGTnWS9veLXmNeyVcM2LZNjFG9dM/ofT0cYS2fB9jNbz8nudSSixWFSPbF/F89+5lOHkLBWqaXGn2VyYmq8FcFHnPfe2zWwzu6bKsynLdhuIQ91dM8q2xOmRKDnNido1JlaenqpjRVTqKwilCGSAqVc6FnLwWKOUGV8Kw3ts7mZZSAQnBbGg5+oAKqOiqy/nvZVJYrN7p1BW/WRQpsfqWdhhaitE+b8b5WtcNhi5Mm2LZ5IpkLop8aS4HpF4woTwQsfPUZCXnQsUMR+3oyblZEDVUDnnL2OueZlvJwlqX7DjKnFR21eK5MJa1cRQSAsOr29Y6G9eEWLYPD9L5+U8knC8yTLzZMFQL/m07l3Vs0B/hlZ/1EI/oWaNWVptKY0cF0pDYHBZ6To1njUCrFgVHsZWtb2ha9nhNTFZKMt3iw11sUI4CIDUN7cCBVctrO+QvWyCUU4lJhZe8boYXCOU5oobKYZ8rKZbVQqUqCUHbXVuwFdsLcz2TFRPYtI2S108k60vm0K02Apu25X392cWTLPtWtyOYiclqkBTK97akdOGTmkb31/Kfyw95XfzjQAuaFBhZ3I2iUuEX0+6cxDJAyx2bKKktY+jIReLBzGO9XCt+14RYrv/ht1Ci0bRf62KNSQyhoDuLmbz7/iWvLw3J4Wd7iIYX9xuMR3U27KxHUUSi2MgTZnIkkBTMqqpQ4nZQ2+yi1O2ksq7ErMY3WXVSqqP3P8b0C/3JfbnmJS/FjKZiVQzsisQmln5xtQiJzPKUCiT2eV3+qrc0EvbMZI9KLAOhCrY+uhtHuem2sB7w7rqVmmefQHi9KEYiHUMqCkaRk+lb9+Z9/drmMvrOTKQ1jVJUQX27O+/rm5isJfOF8ge6XmP6E99ecER+83ncEPzvweaMwYuFrORVUwhB5cZ6ytuqOf6N/RhaagqWYlGova55BVfOn6tLLEuZ0bmi5MLZnNoiSGD61j2Mv/Ft6CWuJY/3jM2gxTPk1S1AiETBXrHLgRCCHXtamRwOMNKbWE6sb3NT1VBqCmSTNSNz0UdhBTLA6Zli/s9QI2MxGwDXlcywx+3lTKiExbyXNSm4y+2hP+xIKwi0Csne8ktLchVdtfgGpvD1Ty5bMFucNrRwHJAUV7tovWOTKZTXEdJup/sP/4Lan/yA8qOHAYnvuhsZe8svYRTlXhVv6AZjAz4C3gjFpTm4wNQAACAASURBVHZqW8uob3Mz2uedF7QQOF12mrvM4k6TK4eFQjmTrWe+nA8t75mzKwa3la88XUK1Weh84DouPHMisUEmVnqqNjXgXmN/5TmuCrFcfPY09T/6Fo7hQQy7A89tdzL25rcjLYlwvWGxomYqEiE9umxYLPi37WD4ne9d9v2Xiign7yXh0NPd7H3rFixWFSESHfuqG5cW5CYmhSYplAtc9LGQvrCDz1xsJzovGnE8UMpgxEG7PczFaBGZBLOCwWZnECkFNsUgpitIQCERcX5z9Tjtzks1BUIIOu7ZxsyYj3NPHF2yiK+krpxNb74hGb0wm5CsT/SSBT75KyQcjHHoqW60uI6uGagWhbOvDrP7/k4q60o5f3yEaDCOUBUqakswDIn5iTC5UlhtoQzLayllV3R2lAa4Lk+vfFeDmx3vuR3foAcjplHa4MZW4sjrmvlwxYtlZ/dZ2r78WNIKTo1GqNz3M+xjI/R98PcAmN59KxUH96EsKOxL1NgJDFVFWq0IXSPU1pVzhbWr0rns/DZdMxi+OE3LRrNwxOTyMT8KsZpCGeBH4zXEZOo0ayCY0VV+uW6Efx5ozpr7pgH/PlKfjCorGJSqOn/cfpF2ZyTjOUXuxSPDNlcR9TtaqdyYKOC7mkWyEOIh4AuACnxFSvnpBfv/APgNEv/UE8D7pZR9s/t0YDa0Q7+U8pE1G/gqcPJAP7FInLmpWtcMdODYi70IIYgG4xiGxIjp9J+bYmLIz20Pb0K1mAXWJuubPk9w1YUywIbiICKjm1hiXt5UHGKve5obXf6V9HRLQ7GouNuq879QAbjixXLdj7+X4pkMoGhxSs6/jn1kiGh9I2Nv+SVKzp7GPjGW9pUskERq6xh709uJVtcSq63PeQwlZQ4q60uZGgmk5b4txNATucomJmtJQhzP/2xK9tzbwvsn/3nVjekvhosy5h1HDIUzMyXYFUk4Q9aEAHpCzpT0CwOFiCG5GHZmFcuKVUWxqujRdNcboQo2v/kGpIS+X5zF1z+JUK7OFtdCCBX4EnA/MAgcFkI8LqU8Pe+wo8AuKWVICPFh4DPAO2f3haWUy6twXufEYzrTEyEyxTTCMzGEIlJcMaQhiYQ0hi96TEcMk3XHwo6qe+5rydpRtZBYBPz31n7+vrcNXYKOgl3o2FWDv+nqptq2vFX2K5ErXiwXDQ9k3C6Fguv4Eay/+BmWgJ9QWydWz2TGdAybZ4rA9vy+E3be0Ur3iTEGzk+hxXQsNhUtljn1Y2o0wIwvQknZ5VtSMLl2uGQj1Mo9m2fN3HWNLuNwQaqjl6LOFp3NVU4VzHbFoMERRfNmDkEYiJTUjTmiUmW/t4x7Kj0ZzxNCUL+zjeEjPSm5y0JVcLdVIRTB6e+9jBaNp7S49g162PTmG/BcGGPseD/xcAxnVSmNuzoorr4iU6XeAHRLKXsAhBDfAt4KJMWylPLn844/CPzqmo5wjYhFtKxpORIyWw7qBuODflMsm6wr+jxB9tzXcmkuB7riB1ZdKM+xs3SGz246y3NTFYzF7GxyBtlbMY1TLVwztvXIFS+WtRIXNs9k2nZh6NQ89WOEYSCkxLBYEVk668XL8696VlSFjTvr2bgzEZkOTIc5+NT5jJHmSDDOoae62fPIZmyOK/5XYLKOSSn66DyGtu9Acp/3hX5Wo5hvIW+tmeD1YMmCVAyJVUjurZziFZ+L8yEn+jwbOYswqLLGGIvZMtrL2ZeYmGu2NyGlZPRYb/IZrNxYR/MtGxg52ose01LKtaVuEJkOcvHnp/EPTiVFdmBomnNjR9nw0A5K6spX/o9weWgE5kcTBoHFOiz9OvCf8352CCFeIZGi8Wkp5Y8ynSSE+ADwAQCXe/U/TyvhzJGhrPuEmP0oZNDSNrs5P5usH+YsPjPN5WshlOeotsV5V/3Ymt1vPXDFzwQT9zxI3ePfTfHhlEIgNC0ljqVoceYsNedv1202/n/23jy8reu88/+cc7ESJEhw3xeR2i3Zkm15j5fYieNszd6kk2YmkzpNJ12mbZ6ZTvpLp51JmzSdtE07XTKdtok7SdymSerEju3YjmQ7knfLtqxd4r6TAEhix733/P4ACQEEQIIiJHG5n+fRI/LuIHAPvvc97/t9J95e+lS8Cp+bq2/t5LVn+/IKZtM0GTw7TfdVa/PLxWJjsHTRx+X57O0sD/PJliG+MdICpLo++ew6v9nZh10o7qud4p9Gm/Anbejzwlgp2F0eYtpfzeKJPac0uKs6f1R5ASEEjXvbabiqlWQ0ic1pS6dYzA5O540ymrpBsG8yz3KTwefOsvPnrruIV78+EEL8O+A64PaMxR1KqWEhxBbgKSHEG0qpc4v3VUp9Hfg6QFP7jjVnTpyI6fjHChcbde2qp+9Efvu4tm2WI4bFlWNxusWCxWfwS5+7ImP5Zmbdi2X/rXfhnBin+vDBlPuFMjHtDmQ8hpbM/poVgCklSrOhNA1hGkzc+15m9h+4JNdW3+qlsqaMwESuMbdpKGamInn2srAoDf3+0GUp+iiG26uD3Fw1Q3/MhUuatDjjvBn28PvnujGVQDdJC2UQGAh+6q+m0ZFgKulAV6myErtQ3FA5w/6KuaLOK6TMai6iTLOwS8YSXTUjU3MopdabreMwkGlK2jq/LAshxN3A54HblVLpvByl1PD8/+eFEAeBfUCOWF7rJOI6QorUU9oipBS0bKnG7tA4c3TsQiRFQfeeBqpqLRtBiytDulHUZ3dcWKgnqc0RyhaXg3UvlhGC0Q98jMl73ol7sA+9vALX8CBN3/9O3s1Nl5vBj/8SRpmHWHMryrH6Ll1KKSaGZuk7MUF4No5ppDr0pYpE8ie8CwmeSqtDmMWl4XJVR68Eu1T0zFu9BZI2/qSvk7hZuKDORDKecPDrHQP0Rt0YSnB95Qzd7uhFVVqbusGph18lNlPgIXWJmKi0aetNKAO8CGwVQnSREsk/D3wscwMhxD7gb4F7lVITGct9QEQpFRdC1AK3kCr+u+yIRALbbBC9ohLlXPmY6S53FFwnNYHL46BzZz2NHT4mhmYAqGvx4vYU3s/C4lKSFsq/3E3nM1/NWte3Bsbyy0l4ao5YIIyr0k1ZnfeKjcPrXyzPo3srmdt9NQDJqmqa//X/5WyjABmP0fEPf0Wy0kf/p36VeGPzqs997LlBxvqDWdN40VBiiT1ACkG7VThiUSJSXfhSGEpdturoi+Wg34eplh/0jHmLubdW+3lkqoYHRpppdMa5r3aKDnd+N4wFlFLosSSaXUPaNCbeHCLqD+ePLC8RVQao3b5yl5wrjVJKF0J8FniMlHXc3yul3hRC/AHwklLqIeArQDnwL/NfQgsWcTuBvxVCmKSsrb+0yEXj0mOaNPzoX6l9+slUap0y8d94G6Pv+3nQCn91JeI6A6emmBiaxe7QaN9eS9dV9Zx/YzxrjJaaoGdvI1KmPoeuMrtl6WlxxckSyulGUZsPPZ7k7KOvEw2EWBignV43W99xDXb35X+Q3TBiORO9sorxd76fhkd+gNCTCKXSucrCMMAwcEyOs+UvvszJ3/+TdPOSi2FmOpIjlJfD4bSx99b2JSMeFhbFkqqO7sxYYq5poQwwmXCQLKJlKkDMkPz26W3opkBHcjbi5rlgFb/SNsANVbN59wn0TjD43Fn0aOqhtbK9lqg/hCpQ5Ltcb9aW67cUda1rDaXUI8Aji5Z9IePnuwvsdxjYc2mvbmkaHv4eNU8/mWUNWv3cswjDKNg0KhHTOfzIaZJxPW0FNzMdoXmLj53XtXDujXFikSQuj52ePY20dFdfltdiYVEMqaDH+hbKpoLz8zOB3e4oNnlxZQz9z5wkMj2XFdyIBiL0/vRNtt23L2vb8OQsY0f7iQbCuH0eGq/pKLmD0YYUywBTd91LeMtWqn/2U7xvHMUWzZ56FYBIJvG+/goz+5cqEF+aiaGZFQllgOYuH97qlbdqtbBYzEIU4tPbX08vU7q+poUywDZPhMMzVUumYQBIFD8LVhEzJQsJpSaShIKvD7VxbeWb2BYFqGdHAvQdPIGZIYxnBnIdc4pm/aVfrHtEMknN00+iLfbQTybwvXCYsXd/CLMsdww9f2ycRDyJyngmMnST4XN+Ou7bzu3vswr2LNYm6Y6q61goHw95+PP+dhJKptP/P906WDCoUQgjoTMzkKcQWylC47MkI3HsZamUrODAFOeffDMdCInPRpkZ8rPlrt1UdZRupmjDimWAaGc3w53dlH/htyCam6co4zHavvG3tDz4TaZvuYOJ+35uvkhQ4ZgcR0lJsqZuyS/L5dIt8tF/eoqJkVluvm8bmmZ1h7IonsXpFgtRiMAXP5e13VoVyrqCH03W8fhUDQlTku1Ps9irRtHqjNEbLVu0/MLW5yNlbPNk39sjL53PEsqQajKBACHFsm2wFyM1QXw2iru6fEX7WVw8trlZCoX7lU3DEZgmlkcsjw/OZAnlCzvB5MgsHu/a6AZmYZE5lgPrXij7kzb+OE8dyl8NttPoPLts2lwmRkKf1115ugVKgR5LYi9zopRi4NlTOTOGyjDpf/YUle01Jctx3tBieYFYazv2mUCe7n0ptFiU2qefwDU2wtTt99D2//4OGY0glCLhq2HwE79MrLU957hKKaZGiqvKz9rPVMQiCcb6gtY0oEXRLESRb7u7a36JyV09ttTgukbF8WK+2tfJm6FyEukUjAVDRzJ+X7gzBUNxF4UclRWgiTw+5sECBXxCoDltmEkj7aMsbTKrcUne8yiFzWWlTF1O9Aoved5aAIRukPDlHzelVuCLUYAmrRkCi7XBhUZRXelld23V6Dz8tXUplKFwHUpCCR4YaeJ3u3uLPpa9zIm0SYwCaXPOytSDcmIulhLWeTCTOvHZKK7K0szibwqxPP6O91J++gQiWTgKLJNJyk+9Sfmp40j9goOFc2KMLX/xZU594csYnuzIUigYy2qRuhJMXTE5PGuJZYuiSBd9fKaHrfJVIJVuoR85sm4G196Ie5FQBhAITDyaScjQWBxBNpGIeUG9uGW2U5p0uXNbxzvKnUT9uQOo1CQdt2wnEYkT7JtCaAK720FoYpb4TJS8vZCloKKxCnuZJZYvJ8puZ/rWO6h+9mBWKoZptxPcfwNmWX5Lt5buGs69MZaTGqcURMMJ+k5M0NBWZdWLWFwx0ukWGWM5QPLZZ9bNWJ6PsYSzQB2K4Hi4nEDShs+eX9jm7CEFrQd6GDx8OmuWUNokTfs7kfMz8tImUfnGbVJBjgVv/VKwKcRyrK2Tvvt/neZ/eQDn1CSYRp5JXWC+218mqaJAHd/zzzJ1173krFwFVvc+i2LIqo4+/DUChwbS69ZqukU+TkXK8kaJFZKQISh0Q2mY2GWqcCSuNOzCRArFb3T0ky9Y2LSvk75DJ7IjxgJsTjuV7al21y5vGeeeeAOlVKrVcZ5pJyEl7moPXXfuvtiXbHGxGDqYJtLQL8w7SElw/w2MfPjjBXfr3FHL5PAsc4Eohm4iZEooK6XoPTGJEIIzr42x7ZomOnZYKRkWl5fMjqrreSzPx1Z3hGcDvpygxgI/C1Txrvria0dqtzehOW2MvHSe+FwMh8dJ8/4uqnsu/J3sZU7c1RVEpmZzMjbcvvIsj/3VsmnUWnjbTibe/m4af/hd7MFA3m2EUjliGVJRZ+foUM7y8koXNruGscw0bj6kJmjtsYpNLJYmf3X0+hxUyzUDm1DoK5yMkQJ+pa2fN+cq6I256XBFeV/DJFUFohS+rnoSoTgjL58HkcpRdleV0X3PHoQUmIbJuSePZYtplTpRRVMV9btaUEbKpqistuLiX7DFRdP67X+g8ujLCPPCe2RoGpHO7iXdi6QmOXBPN1Mjc0yPzhGP6VlF2GreGen00VFqmyvweF2X+qVYWADZQvn+ntc4+831O5bn41ZfkH8caclbaaAQzBorl5u+zjp8nUs/1G65cxcnf/jKfHqdgbRrSJtky12lDXJsaLHsGuyj6tUXwFSYDgd1Tz2WZUOUiRIC0+5AJJPIRRUiht1BrCU3Z1kIwdW3dvDyT8+jTIVpKqQmkFJgmiqvS4bUBCjYtq8Zb7W7NC/UYkOyEaqjM7mucob/O9ySs9wpDJzSZNbIJ4IUphL87VA7uhKYCs5EPDzlr6HBGefddVO8xRfIqcFt2NNG3c5mooEwNqcdp/fCvTY3kv9hGVMRHp+h6h3XrOJVWqwIQ6f6ZwepPvI0QteZ2X+A4P4bqXz1RaSe/TCkJZM0PPJ9Aje9ZcmiayEEdS1e6lq8vPTkubzjsDIVI+cDbL1m/flnW6w/Fgvl4JcfZCMJZQC3ZnK7b5qfBmpYPFXnkgY7PbmdjEuB0+tmz0duJNg3RXQmjKuyDF9nXUlTMGADi+XG732bmsOHEHoynYuYb3hVgOlwYFR4GfrIJ+j4u7+ARCJrvbLZCBy4Ne95fPUebn33DobOThOaieGtLqO1p5p4JMmJl4YJTIQRUlDXXIGv3oPNrlHX4sXpvnhvZ4uNT2Ze23ou+sjEJRX/pauXP+5NFbWYCkwEN1bNcIfPz5d6u0goOT+NtyBwBDoiJ03DRDAcd/MPw82ci7j5ZOtIzvmkTcvrtanMwjNBK3XKsFgFpknnX/8pZX3n0nnJjid+jO/woVT0WM+dObCFQ4hEouhOfnrSyLtcqcLrLCxKzWKhvN5TLgrxocYJXpytImxo6XQMuzBpcca4umLlZgjFIm1aVnrGpWBDiuWyc6epOXKoYBQ5E9PhYOw9H8Z/650gBH2f+S1av/V/sQcCgCLW2MzQx38pr6fnAq4yOz17G7OWOZw2DtzTk04+X4etci0uIylxnO0KsZDXthGE8gI7PBH+Ztdxjs5VEDY0dnrCNDpT9+mXtp3hLwfaOBdduNcy75n8909caRwMVPPu+knqHPlbyy+mvLGqoCiuaPGRjCSYHfYjhMDbVo3NaT3YXgrKTx2nrP98VgGf1JNokTCywPtj2h0oe/HvR31bFXPBWE50WbNJ6lpK27TAwmKBVJ3JBTaDUAbw2XX+aOsZ/nmsgVfmvDiE4nafn59rmMhbX7Ke2JBi2ffCzxCJ4vyPhVKEtu9OT+tFtmzl9Of/CNtMEKRE91YWfd5EXGfknJ8Zf5TyShetPdVWBNliWS7YCHVw1475dAFD33BCeQGHVByozDWp9yftDMXcrLRyVhOKE2EPdY5gUdvbnHZar9/C8Evn03nLQgqkTcPt8/DGg4cRIlVtrZSi/dZt1G61putLTcWbr6Elcj/fmq5jOF0IHaRxIfpr2B1M3XEPyOK96du31TB0dpp4JJl2LpKaoLLGTU2TlY9uUXpSHVXbL4zlQE/yyIYXygvUOpL8Sntujdd6Z0OKZZFMFvV1a0qNaGsHifpFH2Ah0Kt8KzrnXDDKc4+eyYpgnD82zvX3dFNV6yESinPm6BhTI7NIm6S1u5otuxvQbFZTks1MVi5b91H0Z46k121EobwUj03VEM/j07kcAiiTKyuyrb+qDXdNBePHBkmGYpQ3+yhvqKTv4HGUoVBcEGkDz56mvL6yZH6dFilMlwslZVYR3wKRtk6QEs/5MyibDaHrBA/czMS9713ROWx2jZvesY3+ExOMDsygSUFLTzVtPaVrVmBhscCCUF48lgcPDWwKobyR2ZBiefbqa/G+8WpO1MIUAiEkpsOBMAyiza30f+qzJTnnS0+ez5nqM03FKz/t5ab7tnLkx2fQE/NfwEmTvuOT+MdCHHhbjzVob1JyqqO/cPBKX9IVJajbuRg/RgEryoeLTM0xfmyQ+GyU8sZK2m/ehsPj5PxTb+ZtUKJMxdSpEVoP9Kz42iwKE7z+ZmoPPp4jlg2HE/9b3srs1ddi909hDwaINzTl+NwXi92h0XN1Ez1XW7MDFqUlX0fV+7uP5okiW0J5vbMxxfKefUQ7unD3n0ebT8dY6AumpCC4/wDTt99DvLG5JOeLhuIkYvltrJIJg9OvjmIsKiYxTcVcMIZ/LGRNB25SNnp19Eq5umKW3qg7j7F96iHUKUySSqZc3lDYZWr557p60z8vx/TZMfqfudAeNTI1x9TJEba/+1qSkQKpW0oVXmdx0cQbmhh71wdp/OF3QSmEMlGajZn9NzC7dz8AyepaktW1V/hKLSxyKdRRNfilz1lR5A3IhhTLSEnvZ36T6iPP0Pyv/y/VbARSA7Ku43vpCLN79uWIZS0coqzvHIbTRWTL1qJz48JzS0+XByYjeZuDGbqJf8ISy5uRfn9o0xR9FMvbavw8Pl3LnC4wCxT3/UZ7H+WawaPTtfRF3bg1g96Imy3uKI5lBLOpGww8ezotlCEVNTYSBgM/O01lWzXhydms9ZCqtPa2WJ02LwXTd9zD7J59VL72EkLXmdu9N69NZ7EopfCPhxk+N42eNGlsr6Sxoyrd8cvCohQU6qga/JI1lm9UNqZYBtBsmE4npt2OFs8WszKRoO6JRwjt2pteVvfYD6l//EcomwYq5ZLRf/+vE23vWnzkHMorlza2d5XZiUdyK/WlJqwufpuQfn/YEsp5KLcZ/OHWM3ylt5O+PIV+caXx1YFO7EJhKoWBBkn49qiTZwI+fr/n3JIR5vDEbMEsj9B4kC1v3c3EsSGSsUTauU5Igd3jwLelvkSv0mIxyZra3O6oF8nJl0cYPjuNMZ8S5x+fo//UFAfu6bHqQyxKwkbpqGqxMjb06GEPTCMLuGI4AtPpn72vv0L9Ew+nbItiMbR4DPvcLF1/9SeI+PJFVq4yR8EGIzWN5XTurCs4UDd1rKyQ0GL90e8P076/Lv3vtrstoVyIarvONk+EwrnLgqSSKaE8TxKNgZiLw8GqJY8tlvIuUhAcmGLrO66mZmsTmsOG5rRR3lhFw552TN3y5F3rzAWiDGUIZQBDV4RmYgyemV5iTwuL4sgSyvONoqbCDel/FpeOZCTOxJtDjL3WT2Q6dNnPv6HDmtHWDkybDS2ZHdVVQhDJiBjXPvnj/KLaNKl8/WWC19+85HlikSSmkb8a31lmp77FS9vWGgZOTV34wlaKq2/rtCLLG5yFwfXT219PL1O6bgnlJdhTHuLpgI+YWXwHJgPJT6arub26QHc+wFPvRUgJ5BG+AoaOnEUpk7Ybt+L2eRh+6TzhiVnCE7MMHjljWchdCkwTLRzCdLlX5J+cj/HBmbzd+kxDMdLrp3Pn0m1zLSyWIlXMpzZMR9X1xNSpUQYOnwbmu2++0oevq47O23deNoOEDa3UZDSKTOamPygpmXj7e9K/24P5v2BlMllwXSYv//Q84dn8N85YfxB3uYPt+5tp317L9NgcmpYyxLfZS9uO0WJtsSCUH/h0gMAXH8xaZwnlwuzzztLkjDMUc+Up9iuMP7m02BJSsuWtuzn3+Osoc1EnP0U6ejx45AwI5i3kLjDw7GnK67y4qjwreTkWBag68jRNP/wucn72LnjtDYx88BdQjuK68+Uls/ljzgoLi4sj3VHVEsqXnUQoxsDhRbUmhiLYN0mgteaSd+5bYOOmYZgmLd/7Vv4W13YH8YYLEaJIVzcqz9OJabdnRaDzMRuIEp1L5C3gg1RUo//EFABuj4P6lko8XmfB7S3WL/3+cNa/hSjEQhTZmq7LxlRwKlzGSzNegskLz+2agC90n+OddZN4tSQF1E8OjiK8lr3NPnZ/6Eaa9nXgqMhfa6BMhcoToVSmydSp0aKuxWJpKl9+jpZ//Ra2cAipJ5F6kqqXn6f97//qoo/Z0F6JzJNqIzVByxYr3c2ieIYCkayx3BLKV47pc+PkE0ymbjJx/PI1P9mwkWW7fwqRpzsUAIaBY2qCxLxgnrj3PXjffC2r658pJYm6BsLbdi55nngkmXrkWCKlUU8a6AmDY88PMjk0i9QEpqFo6a5mx3UteQd4i/VFWhx/dseFhXoyNbha4jiHoZiTL/V2ETY0BKArwV3V03yieRQhwCUVH2kc58MN43zu9DZG4w7MJZ/tFdd5c7sC5sPhcdK0r5O5sSCJuVjxF60gEbG+KEtBw8PfRyazU9+knqT87EkcE+O5jaKKoKLKTfu2WgZOT6XTMTSbTHVT3VpTkuu22Piko8iZY/kG7qi61jETBsrMHzBZbMl7KdmwYtl0ufN2hgKQyQRN//othv/dp9C9lSRq6kn4anBOjCEynmBidY3LnqfC58obhcrE7bFz7LlBJodnMU2Vbrs6fN6PZpNs318av2eLK0NW0cczX81aZw2uufgTNr5wtpuoqZE5PX7QX02rK87dNf70MiHgv205z1d6O+mPuVAIcqfUFS5pcl/d1Iquo7KthtDYTI5VXCGkTVoWciXC4c//XinNhnN85KLEMsD2/c3Ut3oZOufHSJo0tFfS0F5lBSQsiiKzUZQ1lq8NvK3VjL3en3eC0e27fClxG1YsG+UVRDp78Jw7lSWAIfVVW3HqTbb+0e9y+vNfpPLVl3D4p7O2k6aJ982jlPWdI9JVuHOXq8xBQ3slYwNBVJ7vXKkJOnfVcerl0bRIXsA0FINnptl6daPlA7pOyVcdbVGYx6aqeWCkGSOP6I0rjR9N1mWJZUg5ZPzRtrO8NlvOnw50kDDlvGgGUFTbkvxu93mq7fkbAy1GmSYzA9PosSTSpqU6bxWIXCwgpMBe5qTaspArCXqFF/vsTM5yYRqrbkLiqy/HV39x3f4sNi+ZQvmTU39jjeVrBFeVu2AmXmSyuNnEUlAShSaEuFcIcUoIcVYI8V/zrHcKIR6cX/+8EKKzFOddjsFfvB+9vCLv31kAWjRC7U8fp+qlIzlTgpAq8PMefSln+Vh/kMOPnOKp7x7jxSfOUd9WiVZA7JqG4uzr4wXrS5RSJBOWLdV6xKqOXhn9URffGm3GQFLohpjVCz+/X+0N8cfbTvMWX4Aae4IWZ5R9FbPUOpJ8b7yec5H89o2ZJMJxjv3z8/QeOsHY0X5M3UAIUiHsAmgOG7U7mtnxghxm3wAAIABJREFU3muRNqsotxRMvPU+TC37b6mEJNbYQqyl7QpdlcVmJVMo39/zmjWWryGi02FkATOE+Fwsu1D7ErLqyLIQQgP+N3APMAS8KIR4SCl1PGOz/wgElFI9QoifB74MfGS1514OvbKK4LU3Unvw8bzrhVJ4jx1F9xSOQqhFA/r5N8c598Z4OifOPx7CP760518yXlgMSymwOzdsgH/DYlVHr5wn/dXoy9TqdbqjS66vdyT55bYhRuMO/r+zPYwnnOhKciZSxgszVXyyZYjbq4MF9+97+gSJcCwdqVguBUPaJK039FC73bKMKyWuidE8RTsK/w23XZHrsdi8LBbKwS8/CFh1JqVkKmHn26ONvDLnRROKW6qCfLhxDI+2vNC1uR15C/wgNT4vFegoJaWILB8AziqlziulEsB3gPcu2ua9wDfmf/4u8FZxmczxKt58bcn1hruMwI23YeSxK1I2OzP7D6R/15NGllBeLZom6dpdb+XTrQOGApGsf4Yy+b3P9FhCeQXM6rYli/QcwuSjjWNFHesbw81EDA193lpOIUgoyT8MtxIz899PRkInNBos1lwjhRDY3avz/7XIRgvN4nv+Z8hFESGhFPU/+WHBL0YLi1KxeCy3OqpeOmZ1jf92ZivPzVQRMzXCho2n/NX83tke9AJjdSbuag+OclfOZKTQJLXbm9eVz3ILMJjx+xBwQ6FtlFK6EGIGqAGyqjyEEPcD9wN4fav/wGrhEI7pqYIOmwqYveoagtfeSNXLz1N2/gxaIo4SAmW3M33rXcRaO9Lbz/qjSClWLZalJhBC0LWrnq5dVg7kWic9oN6dYSOoDKs6eoXsq5jj6FwF8ZxmI4oaW5LPdgyw1RNZ9jhKwRuhioy85QtIoTgZ9nBNRe5sj6mbBdOhhBRpb+XsfQwr9aLEuEaHUTYb6Lke+PbZGUQysTqvZQuLJej3h3LGcksoXzoem6olZkrMjMFXV5LppJ0XZ73cVJVbu5CJEIKet1/N6UdeRY8mU+O0qaho9tFyoPtSX36aNTX/r5T6OvB1gKb2HasOLwg9CVLAEpF+mYiDptH36d+g4sQbeF97GWW3E7j+ZqKd2W+E3aHlLeJbCVITXHvnFqrqPFZEeR2QGXnI7MKXfPYZSyivkJuqgjw0WcdEQqQjwjZhUm1P8uWtZ3DlmZKb0zWe9FdzbK6cOkeSt9VO0emKIYQClf/+KTSo2dx27G4niVCuXZy0aXjbqwmcncheoeDs46+z4z3X4q62isZKQbLShzDyp6aZNjvKZkXyLS4N/f5wzlhudVS9tLweKs/bXCpmahwLlS8rlgGcFS6u+vCNhMZnSIbjuKvLL6sTBpRGLA8DmRUZrfPL8m0zJISwAZXAdAnOvSS6twrD5UaG5gpvJObfRCmZ2301c7uvLrhpeZULp8dOpEC3vmIQUqDZpCWU1wGLp+gyu/BZA+vKcUjF/+g5yw8m6nk2UIVCcFNlkPc3TOQVypMJO58/s5WYKUkqiQib/CxQxS2+AJ2uKL3RsqxoBaQCxzs84bznF0LQfus2zj9xLBVlzqCqs5bKthpm+qZy1pm6yejRPrbcddXq/gBXACHEvcCfAxrwd0qpLy1a7wS+CVxLakz+iFKqb37d75CqNzGAX1NKPVaKa0rUNxJtbqVssC/L3tO02/HfcgdIyxnIovT0+8Pcdnc793cftTqqXkZ8Np3UPP7isdrkSLCKg/5q6h0JPtwwxk2+wsJZCEFFY9WlvdglKIVYfhHYKoToIiWKfx742KJtHgI+ARwBPgg8pdRlSEwTgtmrr6X6ZwcLpmLMXH3tCg4n2H97Fy/85CyGbqaMsgUrS8tQUFGVv3OYxZUlJY4z30uVFspnv3AQq+hj9ZRpJh9rGuNjTcvnJj8w0kzI0NLpFgpJEjgYqMaBiUkqMq0riU2ksqF/taMfmyx8P1a21tByQw+Dh09n5S4Hzk8QC0ZyhPICM8MBjISO5lhTk3FLspriayHELlJj+W6gGXhCCLFNKVUS656BT/0qHV//M1xjoyhNQ+g6s1ftY/xdHyjF4S0s5m09M1Hc333UiiJfZu6tneL1uQrii2YCFYKomXowHks4+ZuhNmJKcmd14Epc5rKseuSfz0H+LPAYqejF3yul3hRC/AHwklLqIeD/Ag8IIc4CflKD8GUheO2NVD3/LJqe7cGqgHhtPfHm1hUdz+N1cvv7djE1Mks0nMTrczE2EGTorL8o0dy9p8HyVF6DXMhL7uCuHfMWZIZOj/miVR19hTg6lz8vGQQJUnnESimurZih0x3jzmo/NY7cPNjFTJ0YySnyM3WT8OQcQpN5HTLMhM4b3znCtnfuo6xm3aRjpIuvAYQQC8XXmWL5vcB/n//5u8Bfzhdfvxf4jlIqDvTOj90HSAU8Vo3ureTcb/8ezpEhHAE/saYWktVWlz2L0pCvo2pP8ogllK8Au8rDfKBhjH8Zb0QTChTEVK59aEJJvj3axO2+AGtx4r0kYRKl1CPAI4uWfSHj5xjwoVKca6VEtmwl1taJe6APaVwQzMpmY+A/fvaijimloL61Mv17WYWT8Eyc6bGlLeQA+k9O0ba1BlsB30CLK0M63aL7KPozKT0QOjRAEGuKbi2jENQ5Enywcby47ZUi6s9/n0pNFIwso1JuGuefOsbuD95w2SqwV8lqiq9bgOcW7dtS6guMN7euOGBhYbEUhTqqBg8NWGP5FeLd9VPcXh3gjbkK/LqN7403EMsp9Ia4KQnqtqIbTF1O1s+c4sUiBL2/8ls0/uh7+J5/FpmIE+nsZuT9H131IG0aJm8cGWRicAZVpB9VMqEzfM5Px466VZ3bonQsFH3kVkRbA+uVQCk4FPChoUjmyXXLxEQQ1FdWECZtMq8oLiiUM0iE4sRno7gqy1Z0zo1MqV2MVksipnPy5WHGB2ZQSlHdUM7O61vweK30t43O0h1Vr/xnczPjtRnc4gviT9r4l7HGvNsooExbm03aNr5YBpTDyej7P8ro+z9a0uOeeGmYiaGZnDbWS2EaiumxkCWW1wiZRR/WFN3a4HsT9fxwoo64yrWYWyycncLg6oolCngXIYSgdnszkyeHc2ziit1/uUYma4jVFF8Xsy9Qehej1WAaJs89doZYOJG2a54eC3Hkx6dp6vQxPjiDaShqmyrYtq+JsgrLom6jsLRQtlgrVNt1tnnCnAx55ru5prALkwOVM7iWqDm5kmy+5FnTxHPqTRoe+hdqf/Iwdv/FmXIYuslIb+CiPJddZZY10lpgYXC1hPLaIWZIHpqozyOUU4gMH0ibMPHZdW6uKtyxLx8t12+hvL4SaZMITRZspZoPaZO4qi6vZdEqSBdfCyEcpGpFHlq0zULxNWQXXz8E/LwQwjlfvL0VeOEyXfdFMzYwQyKm5/Q1MXTF0Dk/ybiBoZuMD81w5NEzxCLL57hbrH2GAhEsobx++LX2AdrdMZzCwC0NHMJkW1mYT7XkfR5fE2yKyPICQtfp/Juv4u47j0wmUFLS8Oi/Mfzz/57g9Tev6FiJ+MXl1EhN0LbVKmS5EqQG1BQp1wvFA58OWEJ5DTEYc6WLQBYjULylKsArc5UIUi1T398wgWOFkQhp09j2zn1EpuYIT83h8DjpPXgcY6l7WoDUJB237Ug1MFkHrKb4en67fyZVDKgD/6lUThiXkuBkGGOJvPPMnw3doO/4BDuuK3kqtsUlJnMsh1TNiSWU1w9em8Efbj1Lb8TNeMJBiytGm2ttv2+bSizXHPoJnrOnEfOdRYRpgmnS8u1/ZG7nVRjl3qKPZXesrEBPs0mUUuy6vpUKn3tF+1qsnoUo8oXOTSZ39dgIfulzllBeQ3htesEWqArBXdV+frm9NNGHstoKymorAKhoqiLYN5V3O5fPg7vaQ+PedspqKkpy7svFaoqvlVJfBL54SS+wxLg89lSX1SJS45QJ02PFp/BYrA3ydVS9q0daQnkd0lUWpassetH7K9MkNDaDaZiUN1ReUmvPTSWWa3/6WFooZyIMHd8zB5l6232gLf8n0XWDFx4/m/JZLgKpCa66sY3a5grLBeMKkM5l+0wPW+WrQKprk37kCH2WUF5TNDgTeGz6fNFermj+n73d/EZHP/u9pRM5sWCEYH+BdCwJ7Tdvo6LpypnhWxRPy5Zqzr9RnDMKgMNlpcStJxaEcuZYDlZH1c3I3GiAc08cS+swZSpab+imftelcdfZkGLZNTRA5SvPI/Qks3uvJdK9DYRAC+fv7AXQ+PhD1B98lIm738XU3e+AJayhBk5NEZ6L5+TF5UOI1L83nx/EV1/Otn1NlFdaVdmXi6yij8NfI3BoIL3OiiivTbaVRXhhNp84FSSV4M/7O/i97nNsWUVEIpPxY4MUvJlNmBmctsTyOsHptrPv9i6OPtOfdigyDRMQOcENTRN07rQKrdcLmR1VrbF87TEed+BP2ml1xaiwXZqMLVM3mBn0kwjHGH7xfE6x9fAL53D7PFQ0+Up+7g0nlut/9D3qDj6G0HVQiuojzzC3ey+Dv/hp0DQwc99EAWCaaLEY9Y8/BJpg6q53FDzHSgr7lEoVl4BicngW/3iIG+/dagnmy0D+6mhrUF0rDMacfH+8nrMRD7WOBO+pn+CaihBb3FFemK2kkGVcUgn+baKO/9w5kHf9SokFCj9EA8RmIsyNBSlvqFwv/sqbmpqmCu78wC4CE2FMU+Gr9xCYCPPas/3pbZSp6NhZT11L8al3FleOTKH8yam/scbyNcSsrvHVvg7OR8uwC0VSCe70+flEy0hJm4vMjQQ4+5M3AFCGmXdm39RNxt8YtMTycriG+qk7+DgyeaHCWUvE8b7+Clv/8HfBNJZxbQUtkaDu8YeZuuPtIAuZhSz/CRByPliVU5VtcvyFIdweB4m4Tm1zBS1bqq30jBJjVUevbc6Ey/ji+S4SSqIQTCYdnO8r4yONY7wZXtptQiEYipfuYbOstoLQxEzeokKA2SE/c8N+HOUutr1zH3a3o2Tntrg0SE1S03Qhv7yuxcudH9jN1MgshqGoaSzH6bZSMNYDmUL5/p7XOPtNayxfS3ylr5PeiBsDSXJ+DD0U8FFlT/K+hsmSnMNI6Jx9/A1MffmIdSIUK8k5F7OhrOOqXjyC0HOtgKRh4JocQxpGETIXZCKBFo0UXN/c5UNqyx1JFMzkCEyEGekNMDUyx+lXRzn8yOmLdtewyCWd12YJ5TXLP4w0E1daVjvruJJ8Z6yRY6EKlnogFSjaXKUbEOuval2yBb0yTEzdJDYTpfenxwtuZ7G20WyShvYqmrt8llBeJywWysEvP3ilL8kig+GYk4GoO8svGSCuNB6eqisqVbUYAn2TFIxmZCIE5Y2XJmVuQ0WWpZ4snHu4ApTNhuEq7FjRsb2W8f4g4bnYfIpFnmuRABLDXLqBgWkoYpEk598YtyyMLpLUgJrtC/V7n+mh8/DXLKG8BtEV9EXz31/a/DTeUtiE4ubKILoC2wqn+ZRSBPsmmTg2hB5PUtlWQ8Oedrbddw19T58kPhtFqfzWdShFaCxIMpqwossWFpeIVPrcAqpAd1WLtcBk0o5NKBJ5xsuwoWGS8qxcLXosWZTDjbRJGva0l+CMuWwosTyzdz9VLxxGS1y8QDIdDqZuvyeV31wAzSa54e09jA3MMNYfxD8RwtRNlEo5XwghuOb2Tl491FvUOZWpGBsIWmL5IkhHkT+748JCQ7eE8hpGkhLFeh5RrIBWZ4zBuIvc6HJqsEwqwV8MtuMYUny0aZS7a/xFn3vwyBmmT4+mW1vHZ6NMnxlj5/uuY/cHbyAZSRAPRTnzyNG87a+FlBgJ3RLLFhaXgH5/iNvu7uCuHfMP04ZOj/miJZTXKG3OeMHgRrlmMKfbqLKvfta8vKFyaUtIARWNVbTdtBVnxaWpB9tQYjm8bRehbTspP31iRYJ54c+vhCBZUYnQk9inp0jW1BbcR2qS5i4fQsDU6Gzqe12lhG99u5fJoVnI/K4VF776SzU1sdnJqo5+5qtZ6yyhvHaRAg54Z3hhphJ90fSdWxrc4fPzrbEmUtlpmQPxhZ91JdAVPDDShNemc6BydtnzxmYiTJ0azaqgVqZCjycZfrmXjpu3YXPbsblsCE1CXrEsLtlgbGGxmen3h1NR5O6j6M8cSS8PHhqwhPIapcaR5DrvDC/OeNGzYsiKsKHx2RM7eFf9JB9pGF/KYGxZPPVePPWVhMZnssZvadfY+XPX4fS6L3nx9YYSywjBwH/8LFUvP4/vuacp6z2LNJZPCJ/XuQgEzulJag89Qe2zT9H3qV8lvH13wf1ikSTHnhsk07pZKRjrn0kV+GUsF0LQurWawVO5fq5CCpo6S1+9udHIl25hFX2sT/5Dywj9MTfTSTtxU+KQqZslbGh8d6IRE0ExhbQJpfHd8YaixPLccCD/CgX+02P4z4yjOTTqd7fiqHARjYeyNpM2SeuBbkTBwl8LC4tiyU63gNvuTgnl3CiyJZTXMv+heSTlXpQVBBQowEDw6GQtW9zRosboQggh6Hn7XsZe62fq5AhG0qCi2Ufr9d24KstW+xKKYmOJZQApCV5/E8Hrb8Jz9hQdf/unCF1Hmmb6vcz3FSwgrW6loYMB7d/8Oif+x58WdMUY6w8UjBIv7n2iTJVXKEtN4PY42HKVNSAsxYWuTfmn6KwBdX1RbjP4422neW2ugr6oG4+m808jzSSRRdVxZDIWLy4lQtokQixxeKUw4jqjr/TlXd20v4vaHc0ruzgLC4sc0raeGelzPckjVrrFOuTVUAUaikJhybjS+NFk3arEMszP5u/vonl/1/IbXwI2nljOINyznTP/5Q+offpJnKNDJL1V2KIRyk8eKy7inEziHuon2p7/zUkmjKK7+BWirNzJje/YirZENf5mJ6si2pqi2zBIAfu8c+zzzvH4VA1CFCisWwa7KG6nyo5a1M9Or/wE88yNBmjce2mKRyxWh1KK6dE5xgdmEFLQ3OWjqi7XgnAuGCUUjOEud1BZU2b5Zl8BFoTyA58OoGekz1lj+frkoL+ahFpavwST619qrv9XsAzJ2nqC+w9Q/+gY7sFeYi3tqUhxEWJ5OWoaK+g/OYWRJ7exWMKzMWvAXobF1kHWFN3GY9bQlh1wC9HoTBS1nc1pp/P2nfQdOgEoVJGNhRaIToWW38jisqNMxavP9OEfC6XH4uHzflp7atg5XzSt6wavHuwjODU/9a/AWWbnwD3duMqsYs3LRWajqOCXPmeN5euc4yEPZyNlLJUyJ1HsKl+68dN6YMOL5bofP0TDoz8AUm+na3wMU9MwXK6FRGVkPA5K5dbe22xEWzsKHttX76GqtozAZLjojn6LsWr9liZd9GFZB21otpdFcEqTuLmU0VBuSyGbMLm7Jje9qRDVW+qpaKzEf26c2EyUqVMjRd+E9nJn0eexuHyMDQSzhDKkLDmHzk7T1FlFVa2HEy8OE5wMZ1XTR0MJnv63k9x071YqfIWtQi1KQ05HVWssX/f81O9bxupT4ZAm76ufuGzXdKnY0GLZMTFGw6M/yKmn1wyDaG09ox/6OELX0T0etvzV/0Ikk0g9ialpoGkMfvz+Jbr4pZLO99+5hYGTkwye9WMaJmVeZ2pQLlI8V9eXI0vZE3ID0e8PL1H0YbHeORUu48GxRgZiLny2JFW2JFMJkWNwn0LhEAqFIjkfgbYLk3pHgluqgis6r73Mibe1hpFXXi5aKEubpOnqwg/OFleO4XP+vLN7pqEY7Qvg9bkZ6wvmtZ1SpuKFn5zljvfvRrNZqXCXihyhbLkVbQiipkahqLJAsbd8jl9oHqWhyNm/fCilMHUTqUnEFdRKG1os1//43wquc42PEW3rwJxvPnLq839I9eFDlPWdI17fiP+2u0jU1i97DikFnbvq6dyV2nbg9BTByeWnHKQUSJtk14HWIl/NxifVojqFMd8r3BLKG5PX58r5X32d6dSLsGEjw8QxY0uBXZjYheJ3tpzjtVkvzwR9KODWqiDvqpvEIVc2P2MkdU4+9DJmculULCEFQpMoU9G0v4uqzroVncfi8rBUswJlgq6bSz4T6UmTZx46QfeeBlp7aqy0uBKQOZansITyRuRA5QzH5sqJq+wZQac0+KWWYW7xrSyQsZhA7wRDz58lEU4gpKB2WyOtN/QgbaVodbIyNrRYdk6MLZFJo1BS4hrsp/rwIWyhOeZ272Xgk/8JZb/4Vqg1jeVLbyDAW+WmrtVLc5ePwESY8YEglTVlVDeWb9qBeiHycNvdC8WUpiWUNzDfGGnOk6Oc+dlX2IRipyfEjrIwb62dptJm0FMW4wONq5vS6z14YlmhDOCp89J8bRdltRVojg09VK5rmrt8zExHcmbzFtpb2x0aDqdGPFq4OUI8qnPy5RFm/VF239B2qS95Q3PBuehCYfxdPdISyhuQmytneHSqluGYKz2eO4RBqzPODZUzqzp2cGCK3oMn0r7KylBMnR4jHoqx9e1Xr/raV8qG/gaIdGzBPdSftw9YvLae6p8dovHh74GeRCpF+clj1D71KOf+8+cx3Rfn3efxumju8jHSG8gZvIWEqloP19/dzcx0hMM/Pg0KDN1Es0k8XifX392NzX75n5quJOkpus/0sFW+CoDSdUsob0CUgkOBKkbiy+X/ppqOKOD9qxTHmRgJndnB4nKcHRUuKpot//O1TnOXj6GzfkLBGMb8F6tmk9Q2V1Dd4EEIwfb9zbz+s4Elj2MaipHeAFt2N+Aut4r+LoZ0R9VFY7l+5IgllDcgNqn4793n+Ml0Dc8EUmPlW3x+7q7xY1vhjN9ihl88n9WABEAZJnMjQWLBMK6qXLebS8mGFsvTd9xD9fPPIvRkzrrhD/wCXX/3tSwLOS0RR0xPUveThxl/z4cu+rw7rmvB7tQY6wuSiBuYponDaaNtay1du+pQCl452IuRvPBBMHSTUDDG6aOj7Lp+86RmZOay1f7Rb5PZNsISyhuP70/U82/jdRTTcAQEx0PlRAxJmXbxjjOZ6LEkQoplLR+lTVK73fJUXg9ITXLgnm5G+4KM9AWQUtDSXU1DW2V6pq6p00c8luTUy6NLHksIgX8iREt59eW49A1FplDuPPw1AocuPJxYY/nGIWkKJhMOvDadcpuBQyreWTfFO+umSnqe+MziVJ4UQgoifkssl5REfSN99/86bQ/8H7RIGKFM9DIPE29/D53f+GtEHvs4qetUvfzcRYtl/3iIo0/3pfLoRCo5ffv+Fjq2X2idPT02lzfPzjQVI+cDm0YsW9XRm4uIIfnBRH2q8UiRSKGImaUTy3aPk+X6rgopqL+qjYqmqpKc0+LSIzVJS3c1Ld2FRW7njnoqqz2ceGmIuUCs4HZ2x+aa2btYCnVU7Tz8tfkosjWebySUgh9O1vH9iVR9lqEE+ypm+XTbUMnG50xsbgfJcJ7ZCAWOK+BMtKHFMkB4+y5O/sH/wjkxhpIaptPJtv/5O2iJwlNCRfY4yCER01MR40WV2adeHqa80klNYwWQKigp9HVtGqX/0K1FrOrozUd/1I1NKJIruL/KNYMqW26uacJM3UErLe6TmqRpXyejr/RiFvJHF1C7vWlFx7VYH/jqPdx833bOHh3l/ImJnE6rQghqmyquzMWtIwp1VL0glC02Gk/6q/neeH1WMd8rc17+tL+Dz2/pLfn5Gve2M/ziuexxWggc5U48dd6C+8XnoiQjCVxVZdicF19/tpgNL5YBkJJ4Y2pKte7xHyKMwoUeppTMbdt5UacZ6fVjmrlfwErBywd72X2glcaOKnx1noLWclX1l3dq4UqQqpS2hPJmo8KmYy7pyZmNhsknmkfIdAsajTv4P0OtnAqn7pNtZWE+1TpMiyv7M6RMxeywn7nRAHa3k+ruBuxlDuJzUexlDmq2NzF9ajSvYFYKJk8M03qg5+JeqMWaZ8ueBmYCUfzjIVCp2QQE7L+zC2l1U12SpTqqWmP5xuV74w05rhe6kpwOexiNO2hahT1cPup2tZAIxZg4PoyUAqUUrsoyut+2N68RQjKa4NwTx4hMzaVT7ep3tdByoLskxgmbQyxn4JwYK9jqWgHCNKk6+iL2mSD9v/RrKEfxhR6xSDInUpE+tqE4/sIQ59+c4Ma3b6Vrdz29xycvRJIFaJpkx/6WFb6itU++6TpLKG8+Wl1x6hxxhuMuclsALUbxmbZBbqy6UFEd0jW+cLaHsKGl9z8V8fCFs918dccpKm2p+9pIGpx++FViwQimbiA0wdCL56horCQ0PpsaOAWF85ZNRSwYLcVLtlijSE1y7Z1bmJmOEJwM43DZqG+ttLyWlyFLKFuNojYNSkFAzy8XNaEYjTtLLpaFELTe0EPjNR1E/SHsbseSecpnH3udyHQIlELNS7zJE8PYyxw07Glf9fVsKrGshUNEG1upIn950cIykUziOX+Ghof/lbH3fbTo41fVeuincJK7aSiioQTn3hijZUs1I+cDRMOpD5jNJrnqxja81Rurk1S66OOzOy4stKbrNi2f6+rjf5zrJmRoJExBKiEp169GQ9FTli1Yn/L7SJgyS2grBEkleXKqJu2aMfpqL9FAOMtyCGBuJDi/z9IITeKpt6biNwOVNWVU1lyc89FmxBLKmxMhwGfTCei5aQ2GEjQ5L913uc1pp6JpaVeiyHSIWDCcUvUZmLrJ2GsDllguFnd/L63f+nsck+MU27JL6kmqjzyzIrFc31aJEDnvVxbKTNkTDZ/zo2e4YehJkzeODFJZ68FVVro8mytJVnX0M1/NWmcJ5c1JvSPJn+84ybFQOQNRFw9N1jNnLO4CJTBRPDjWwK91DKaXnol48ngzQ1JJTkcuCJ7p02M5lkMrQWqSuh2WE4aFRSb9/rAllDcx728Y559GmrJSMWzCZJsnXPKo8kqJz0UL6i49lkQptepUjA0vlu3TU3T95R9nFfSp+X/L/elkPJYRR+LSAAAgAElEQVRSvkX+kaUUVDeUMz0WWnI7I2nmPblpKgZOT7HtmvVZXLS4a9NCFMKKIltkIgXsrQixtyKEXZo8MNKMseiGUEheD2VHd5uccTTMnHbYGmZWZGMlRbJCk9jLHCRCqf099V46b9uBzWX57FpsbhZ3VL3trW2WUN7EvLXaT8SQfH8i9d4bSrDfO8unW4eu8JXBzOB0wQCJo8Jl5SwXQ+2hx3MK+hb+bAuCuZBwjrW0Fy2UF1hIq1iKQu1ZlamY9ef3Flzr5OvahDK4v+c1zn7TEsoW+fHaDOxSkW+csy+aBbq7ZprHp2pZXBurCXhb7YVGI5Vt1QR6J4udRGLr269OWcoB2iZrCLSWcY6NUPeTh3EP9JKoqWPy7vuI9Gy/0pe1Kej3h7nt7s6MJVZH1c2OEPCe+ineUTvNVNJOhWZQblu+E+qlJhGO4z87ln+lgJbrtpTkPBteLLsH+goW9C0IZdPhQCiFTF5oXmLaHYy+/6OgFMLQUZqtKOG8XLODpRACyqtcF73/lSKz6OPT219PL1/owmf5bVoUYp93tsAaxZyh8epsBfu8c0AqheM3O/v4y4F2kvOuGjah+JW2waxpwJbru5kdCmDoBixxPwpNUtlWg6vKylm9Yhg6VS8/T9VLz6GkJHjDLcxcfR1l/efp+qs/gaSOVCbOiTHKz55i+IO/QPDG2670VW9oFmw9843lllC2sEt1xdMuMgmNBRFSogrovOru0nxmN7xYjjc04+4/j8xj6QYpwSwMg9H3fJjaZ5/CNjdLtK2DsXe+H9fIEG3/+NfYQnMY7jIm3/oOpu66F2ThiumG9ir6T01dlGiWmqR9W+3yG64hFldHB774YNZ6a3C1WAqXVPxGRz9f6e2cT8VIl9liIPiz/g7+984T6QjG3ooQf7PrOOejbpQSbCmLoC16hnVWuNn1gQOMvzHA5MkRVB57OOmw0bCnjaarV1/4YXGRGDpd//tPcA/2p9PkPOdOU/nSczj8U8jEhS9kAYhkgpYHv0nT97+DAGb27mf8XR9Ar7Sax5SKBaH8wKcD1lhusS6QS8wGag7LZ7lopu68h6qXnwNz6SehmetuxH/HPenfaw49QeOPvpsesG2RMA2PPYQWCS/Z3a9rVz2j/UGSMb1gusVipBTYXTb23txO2RXoTLMarOpoi9VwLOThhxN1i4RyJornZiq5u8afXiIFOU4Zi3F4nNRub2byxEje9c4KFw1XtRGZDmFz2XFWbCwXGiFENfAg0An0AR9WSgUWbXMN8NeAFzCALyqlHpxf94/A7cCCd9+/V0odLeU1Vr36YpZQBtASccpPHUfqybz7CNPAFku9976XjlBx8hinf+eLmGXW7MBqyWwUFfzS56yx3GJd4G3J37VTaILa7Y0lO8+GN5WMN7bQ/8lfQfeUF0xhjDc0YZRnFBMZBvU//kFWZANAJhLUPv0EMlb4i9rhsnHLfdvYclV90Z6dCsWem9rw1XtQS1lprDGs6miL1fDoVA1f6e3izXA5hcptdSWJGBeXRxydDqWaTeRb5w/x2j89w8kfvsKxB5/j2L88RzwUQymFKjALtc74r8CTSqmtwJPzvy8mAvyiUmo3cC/wZ0KIzDDt55RS18z/K6lQBqh86bm8nVS1ZOHARpZnimkio1Gqn3u61Je26VjcUdUayy3WC1KT9LxtD9KmIW0SBEibRllNBc37u5Y/QJFs+MgyQGjXXk78zz/Dd+Rpmr//bTBMpGlgajaUTWPoY5/M2t4Wmi0Y2VCahmNyglhbR8Hz2Z02uvc04qsv56WnzhVsVJI+pgmvHurD0E2EFDS0VbLzuhYcrrX79qQKQNqtog+LiyJiSL492pTXCi4TuzTZVb60u0zBfcudhYv81AX/ZYD4TJRj3zmS7vzkqfPSfss2ymrXrd/ye4E75n/+BnAQ+C+ZGyilTmf8PCKEmADqgODluECl5X8IUkCyyod9JohY5sFFSyYoP3EslR5ncVEsFsqWc5HFeqOiycfej91MoHeCZDRBeX0l5U1VJXHBWGDtqrFSIyWBW+4gvHUnNU8/gXN8hGhbB/7b7ibpyw7jG+7CXWKErhedI1fdUM6u61s5/uLQsoLZmM+rVKZifCDIzHSEW9+9A1kgMna5SQ2omShLKFtcNKfCHjShlnGsUOz0hNniiq7EwTFNeUNlusV1sc4YC7UG4clZTv3oVXa+7zpcletyir9BKTU6//MYy1TZCiEOAA7gXMbiLwohvsB8ZFoplVdFCSHuB+4H8PqKHwuCB26h/PSJnOiy6XAw+v6P0fiD72ALzaHF4ygpwTRz29cIQbJq6YYFFtnkG8stoWyx3tEcNmq3Xzp//M0jludJ1Dcw+sFfWHIb5XAQuO4mfC8dyXbIsNkIbduF7q0s+nytPTU0dfnwj4c4c3SUuUBs2X2UgkRMZ2JwhsaOK1+8ko48ZHTh60kesYSyxUXjkMunOjgxSJiCjx/bgxSKGyuDfLx5FG+RdkVCCLbddw1nHnudxFwURCpqXGzDEtMwGHttgM637Fh+4yuAEOIJIF9S3uczf1FKKSFEwccFIUQT8ADwCaXSj/W/Q0pkO4Cvk4pK/0G+/ZVSX5/fhqb2HUXnkc3u2cfcrj1UHH8DOS+YTYeD4LU3MbtnH7O79+J94yjuoQGUTaPuJw8j9GwbUGWz47/1rmJPuelJeSdnj+XoSUsoW1gsw6YTy0vhHB3GNTSAu/88Zf29mA4nGCbKbkeYBpHOHgZ/8f4VH1fTJHXNXmobKzj0g+PEo/qy+xi6ycx05IqL5awpuowufMFDA5ZQtrhoyqRB3FwqBUNhCsnx+XxmUwmOBKs4E/HwJ9tOY5PFaTJHuYvdHzhA1B8iGU0gpOT0w68Wd5EKwuMzy293hVBK3V1onRBiXAjRpJQanRfDEwW28wIPA59XSj2XceyFqHRcCPEPwG+X8NJTSMngv/8M5aeO4z36IghJ8LobiWzZmppG0GzMXnMds9dcB0CiupaWf/4mSqbSN4RhMPreDxHt+P/Ze+8wSa7y3v9zqjpPd09PzmFnZrNWWqVFEkgCBZIAA7IxwRjbYBHMvQ5cHInXBkuY4J+ECf4ZjAk2ApOERVREYhellbSSNk/OqeN0rqpz/+ienunp7skzO7tTn+fZZ2e6qrrOzFSf+tZ73vf7rl9e4oVMrqPqgrkczI6qJiZLYYplQI3OsOOuO3CMjjC7Xju73GdYbUQ7djJ661tJ1axNHApFcOjmLp58oIf4zNI+hXbnuf3zLJ7LZgplk9UxmbLyse5OCuO7cwK4wpImrFmYX9KloxDSLDwR9nK1b2Ui1lnpxppI8/x3jqzoOJvn/PM9z3IP8A7g9uz/P1q4gxDCBvwA+LqU8r8XbJsV2gJ4PfD8hoxSCGb27Gdmz/4ldw0eejHhA5fiOfk8SMnM7v3oZe51GcbkcJjeExMkYxqVdWXs2F973jkTLSQjjuc/VEo++t4us6OqickqMMWylHTd8ZFMMUmRzUo6hfvsyYIugKtFtSh4KxzLEsvVTd51OedqMIs+TNaTGU3lx5M1/CZUzoymZgv7Fn7iMm2C3tk4zC/9VQS0wpbTCUPlbMy1bLEspcR/dpyxYwOkInGMIp7LpVAsCvUXn7c+zLcD3xFCvBPoB94EIIS4AniPlPJd2deuA6qEEH+QPW7WIu5bQogaMn+UZ4D3bPL4i2I4XYQuPbSu79n93Di9L0ygZ9NzYjNJRvuDXPWKnbjLz8+HpVwUeX66ha6ZQtnEZJVse7FcfvTxkkJ5FpFO43v88KL+ykuRSmoce7SfwER0Wf7LQoDTVSgWNoNcXpsplE3WgZiu8DdndhLULGhLuF8IICkVamwpBhIOFgpqqzCoshZ3qinG8OPdTJ4YXpFInqX5UCeexvOzeExKOQ3cWOT1J4F3Zb/+JvDNEsdvi0TgVEKj5/nx/DlZgp42OPXUCJffsD6tcjeT+Y2izHQLE5P1YduL5YrHHl3WfuVPP7EmsfzUAz1EApmq/uVQ3eRdtk/zWsmI4zlyeW2mUDZZAaeiLr49Ws9AwoFTMbixaprX1Exx33QloWUIZcgkYvx0soo/bh7muYiHlMwXy4qQvKQiUPzgBaRjKSaOD+VZxC0XoQh87TUrPs7k/MI/kfXiLhLAmB6fmSvMDiawOSzs2FdDc1fVulpSrScLO6qe/bo5f5uYrAfbXixLVV00qgyZ2JY16EeJRTFcpW3lShH2x4iGkssWyharwkUvalnxeVZDbnK9aa5I5oYuxRTKJivi14FyvjjUgi4znfhiBnxnvJ6fT1VhUwzSyxDKGQQhzUpvwsnbGkb41mhjxmKOjFD+i7b+ZbthzIyHEIqC1Je3f94oVAUtkcbqOr/zVk0WZ7GAhBDw1IM9GNmHrfhMipNPjRCLpNh92cZZVK2WhUI5eMfdmLUlJibrw7YUy66eM1Q99AtswQBpjxdDUVCW6tolFISx8psuQDScXFFnvgPXtG5KQ5JcXtt7u9ipzDkEpB99xBTKJstGk/DV4Wb0AkEsCOnWTCPlosjcfnnvh8KjgQo+vfs011YEORUtw6YY7C6Loq4goGexL/EZKhFRnB2a3XthtcDeyohUEjWRQPN4V26ovQaq6txFTycUgaIKtFT+fcHQJQOnptixr3bLNY1aKJRNtyITk/Vja33aN4HKX91Hwz3/jUinEGS8kwUsKZjTXi+625sR2o8+gBoJE9l7gMDV12E4F29aMD4YWlaeMoCjzEJ148Z0DSuabpGtjg48PJB73ZxkTVbCUMJBWpYSOKWFj0Au8J6ZQ8lucaoGB72RVY3LXe9DsSgY6SJqXYCj3IVEkgzG8pqWKBaFhsvaUSyra7NtsnyUWIymu7+G9/lMN229zM3IG95C+NIrN+f8qsKl1+/g6EO9SCkxdIlqUXC6bcwEi3viK6og5I9R03juCrAhfz7XpTSFsonJBrKtxLISi9Fwz3fzGo0omoYUgmRjM47R4aLtVSVg2OxU3/cT6n52T05ou3q7qX74Ps5+8CPo7uITZ3AqxuRweNljTMY0HvjuC7TtqabzQP26dfArlm6B1OdVR5uTq8nqsCtGERu4UkgsSISA19ZM8OPJWtILniNtQuf6yuXlJS+GUAQ7X3EJp37yNEZqgWCWkAgs6GQmMr7MDZe2U72rYc3nN1kCKdnxxU/jGB5CyboNKaEgLd/6Cv1OJzN7LtqUYVTWubnu9XsZ6w+SjKfxVZdRVe/mge++kHPIWDBsbEutWmww/f4o197UPveC1E2hbLJpaIbg2Rk3Yc1ClytGi+PCX4neVmLZfeYEUlUhnV9NL6TEPj6GYbWhJgujCQJQEwnqfvpDlHkdpNR0ChEOUfeTHzHyprcXPef4QDCX87YQq13F6bKRiKdIJ3WkzEzEumbQd2KSVEJj/zrkLs/PZXv37mO516WmmQUgJmumwZ6i2ppmPGVjsUgygIrktbUT3FgZoMqWRhFwz0QtaSmQCOyKTos9wc1V0+syNle1h44b9tP9y+eX7NwnFIWumw/grFwf716TxXH292AfG80J5VmUdIq6e7+/aWIZMuK3dVd13mtNXZUMnZkuWBW0Oy14K89dik5GKBfO5aZQNtkM+uIOPtnTkZmzJUgEBzwR/qytH8vWrHtdF7aVWJZq6WVVqSrMdO7Ce/xYwe1eAqnKStR4DFgwsRs65c8+WVIsL0aZ186hm7t46HvHC4r/DF0y3O2n40DdmizkFhZ9BD5x94I9zMnVZO18sL2Pvz2zk5SExQSzXTF4Y91EblK9tW6Cg54ID/kriOkqV5aHuaI8tK6TrpHWEYpALlFyIHWDQN+kKZY3CcfoMHn5L/OwT4xt7mCKsOtgA9FQgsBkNOd+YbWpXPayjnPmhjHrf39b5zMFc7kplE02Gl3CP/buIKLnS8fnIh5+NF7LrfX5jULThkCTAqe6cuvOrca2Esszu/ZRzJLCUFVClx5i7DW34v3IXyANI+92L1WVyIHLcA4PFX1fqZSuqK5r9TFweqoguqyqCk0dlWhpg3SxnEoyQ/31PSfZsb8W1aJSXuXEV1O27Im6WHW0OaGabARNjiSf23OC95/Yiywhlq3C4M0NYwVCuNMVp9MVX/cx6SmNieND+Lsniuctm5xTUlU1JYv50r6qTR5NIapF4YobO4kE4oT9cexOC6pFYaTHj8WqUt/mw1m2eV74s0L5G+8OmHO5yTnhhRk3KaNQ76Skwi/8VTmxHNZU/m2omacjHqQU1NmT/FHTMPvd0YJjzxc2x8h3iyBtNgZ//90YVhuGJfOcoNvtpCuqGHvd76B7vJz6m38gUd+IJBPzSNTU0fOnf0PgRS9BFLGgkkB474GS5/RVu2jqqMyzKFItCt5KJ407KlAtyqJ5ybouOXtsnFNPj/DUg7089ouzaJqOpulMj0UITkZLOm2YQtlkM+mNu3AopQpZJVKCV12fTphLoac0jv/gCUaP9hXmJi+C9zxtQnI+Eu3ajeYpx1gQbDBsNiZe+dpzNKpCPBWZuXrwzDRPPdhLz/MTnHl2jEd/fJKhs+uTLrQU8zuqmnO5ybkiopWOr8b1zMq9IeHj3Z0cDXvQpIKOYCTp4FO9O+iNOUkEY4w81cPQY2eJjAZX5BR2LtlWkWWAyEUHOfWhT1Lx2KNYAwGiXbsJH7wcabFi9U/hGhpg9Na3EW/OtLnN+SpLiWG3o8QKb/aO0eIR51n2XtlEXWs5w91+dF3S0OajtqU8J5JbdlYVjT7nkc1ljvjjHH2gh5A/njHTlxnxfen17fiq5zyg+/1RUyibbCo2sdikJ9AQ/PtIE1eWh1mnutWSjL8wRDqaRC7ThQYARWBxnpuumdsSRaHnf/0VbV/9PI6RoUyanCEZf/Xr172l9VoZ6p5mejSCnp2jpZFxcjnx5DDVjR4cG9htdb5Qbj9yF33mXG5yjthVFs166RfS5cq4sxyLePCnregLYrFpKXj8iQl29hxFGgZImDwxjKepks4bL8romS3MthPLAJqvkslXvG7uBcOg6T//Hd9TR5Bq5ldi2O30vvcDJLNi2T4xhtAK2+wKwDk8uGjDEiEEVfUequqLW8LtPNhAKqkx0rO0A4BhSAKTWcug7MStawZP3NdN58vaUa2Zp7trb2rlts5nTKFssmnsLYsiSuSgzpIwFCZSNurtqQ0dS6BnYmVCmYyNmM3t2KARmRRD81XQ/Rcfxjo9hSUaIVHfhLRtvQeWwTP+nFCeT8Z3eZpdl66fe0omfW7+uaTZUdVkS1BjS/MSX4DDQR9JOVsDJrEJydsaRgHoTziKpmr44kHau5/OCOUshmYQGfbjPztGVdaBSEukEYpAtW0tebq1RnOOaLz761Q8/ihCSsi6XSjJBDu+8GlOfvwzoKqg64uY5YtVNywBUBTBgatbiYUTBKdWl7upKIJrd5Vx8y1tAHSlj5hC2WRTkcB7Wwa5s7+VNArFCv0MKXAoG1/ssZooRf0lrSjqtspM2zKkq6pJV1UvveM5QtdKz+99JyZwl9tp7Khc83lyUeT375l7UUubQtlky/Cu5mFanQl+OllNRM9Yx725foyObN1JjS2FTTFIGPmGCrv9p1Fk4dxvaAaTJ0dwVJTR96uTJEOZYGBZjZf26/dumeZQ214s1/7P96n8za8KbusCUFIp3KePM7P3AMn6RgyrHTVZOGGlautK+iyvhK5LGnjy/p5VHaulDdJHH6bdOwVA8OEBUyibbAoxXeGrw038JliOAbgVHbswiOqWvGI/BYMdzjg+68bnLdfsbWTg0dPLP0AIXNUb0wzIZOuSTukEJ6OoFoWKmrKSD1l1LeX0nZwqulohJbzw+BDVTd41+S9nmoxko8iPfDZvmymUTbYKioBXVk/zyuri+fpXeMN8TUgSSOYHTOwyjVKqviqlcfreZzDmPZTOjIc4ec9TXPSmq7ZElPncj+AcYgkGqHnw56WNrqTEEg5lvlYUhn7vXbR99fMITUMYBoaqIlULQ2/5wzWPJRZJcvyJ4VUf71Q0qkZm5k2qplA22XikhI93dzCQcDI7MUYMBZBYhMQiDFKGglUYGAjOxFy8/bmLOOQN8fbG0TULZykl4SE/4SE/qk2lsqseR7mL6l0NDD/ejb6wGUkpBMT9Ucqbz70Lg8nm0Ht8grPHxlCUjF+sqipc+tL82o9Z2vfWMtKbaVpSFAmTw2GaVhBdLtpR1Uy3MNlCaBLum6rifn8VSUNwhTfMb9VNUG4pPa/aFMlHOrv5TF87Ac2CAhjArk4PyoSCoeVHl4WqoNhUjCIN4XRNZ/rsGLX7mtf5J1s521osu08fX9T2TUiDeHsnAI6hATwvPMvMzr2IVApFSxNvbWfq+pevevkwHk0Rmo5hsSgcOzxAOrn0jX3WVUOfd8FZFInXonN5eWhV4zAxWS2nYs48oTyHQJPQYEtyqTfMz6erSRsi+7rgsZCPU7EyPrv7FLaSDhqLY+gGZ3/2LNHJSCYioQjGjg3S8qJOavY1s+/WQzx3928y5dlLYOYrby8mh8OcPTaGoctcYbWuGTz5QA8vfcM+LNb8JWSbw8I1t+ziwf9+oej7GYbEWKLpzXyKdVS9Yac6r6Oqicm5RUr4dG87J6JlpLL5yb+cruJIyMcdu07jXUQwNzuSfHb3KYaSduK6SrszjlVA71AVocHpnGAWqoKtzA46RedpqRnEpmY25OdbKdtaLEurtWQesgQiey9Gc3vo+Mzf4xroBTKSQLfZSNY1MvaaW5E2+8rPa0heeGyI0b4AQhEYulHM/rkotc3l2BvLGDs+STKURBWSKzwh/rBp+ILunmOyNXk0sJjVmmAw6WByykpa5ucw6whmNJXDQR8vXWVr68kTw8xMhOc68xkSiWTwsW7K26qxlTm4+C3XMHjkDKHBaYQicNf5CA9PIxcUaymqgq9t6+bMmqwvvccnirsPSclYf5DmrsIVBkVdfIJ1LNNzeVYof/S9XexUns69nn70EVMom2wZTkbLOBmbE8oAGgozOvxkspo3N4wverwQLGiDLdhxw3783eNMnRjB0HQqOmqp2dtE/69PEZuOFL6HquAod63Xj7QmtrVYjuw9gCiScC6B6I4uBt/2TnZ+8u+whQJ5cTM1lcIxOkzVw/czdfOrV3zevpOTjPYHMm1UV1Cxb7GpuNvLUZ0q//4f19P86F0M/Cy54TZcJialWeriEySkWnS/pFQ5GS1btVieOjVaooW1JNA7Sd1FLVidNjpu2J+3dfy5QUae6snM5lJicdroevnFZnHfNiIRK55OoWuSxCKpFtlLpgChiGXlK89vFNV++E4CDw/ktpk1JibnEn/awuOhcjQpuNQT4bkZN8kirhaaVHgyXL6kWC6GEIKqrnqquuoz75VM0/vwCcKDxfOfhSKo3l2/4vNsBNtaLBsOJwO//25a/+PLIA0UTcPItsR2DQ2w8x8/VCCUZ1G0NJWPP7oqsdx/cnJxT+X551EFwiKwu22AYPTEBLf90S5aD9/FwM9NoWxybrnCG+J+/1J5mqUuUkmVdfUWcsWFMkij9DaAugMtVO9pIDYVQbVZcVYuvyumyYWBr8ZFPJoq6LatWhTKK4tHsixWFW+li9B0rMg2BW/F4lX7Czuqnv16ErO2xGQr8MvpSr4x0ghk4nffHaunzRlDRaIXmb9d6vp0RD3zs2eJT88UFM4KRWB12ei44SIsjq1hJbntQymRA5dy6sO3M37LG4m1tIEQKLqOkk5hD/oXj5sVSUhfDsvJTQYQqqD1qkb27alARhPEpuPMjMf44qeP8udfrFt26oaJyXqiS3g85OUrQ42cibqotqYpUB3L5Io15NlX7Kgp6l4gFEF5y+KFeqrVgqehAleV2xTK25DOi+pQF6wkCEXgdNuobijtinLRVS1YrHNdV4XIBDQuvqZ1UbvChUI5eMfd6/ODmJiskdGkjW+MNJKWCmmpoKOQkgr98eIPf3ZF5xVVa+9cGZ2KkAhEizrMWMscXPS7V1NWu3aXsfVi24tlAK3cR+DQNThGh1G05VXnSyEIXv6iVZ3PU7l4BEJRBapNoelgHa+9qpIzxyeJJ+a2x9MKT0e8PBNxr+r8JiarJaEr/N2ZnXxxsIX7/NX8aLKWQNqCR9Eg1yR+eahImtbQnKTuQCtWlx0xT/QoFoWqrjqcleZnw6Q0ZV4Hh27upKIm43yhqILGHRUcurlrUdHr9jl4yWv3sGN/LVUNHlp3VXPNLbupblz8pr5QKJspFyZbhUf8FUWzQVNSpdGexCoMbMJAJfP/i8pDXOMLrvm8iUCUUquO6VhyywUx1pSGIYSoBO4G2oE+4E1SyoIERCGEDjyX/XZASvm6hfuca5yD/UiLNdeUZDEkkC6vYOplr1jVuXZf2sAT93dTJF0aALvXTuuLGvnY+3by+T//Hol04Y0/LQVfHmrh83tPmIV9JpvG9ydqGU7a0WTWlSX7vB3JOl0UQyCzEnpuuwWDq33BVTthAFgcVva98Uomjg/hPzue6/ykJdNEJ8OU1WydqITJ1sNb6eLQy7uQUq7oxmx3Wum6ePE8ykxzkTlMoWyyVYkZakFr6llsiuTOPSd5PFRO0lC42BOhzZkouu9KcZQ7KRVcsblXbpyw0aw1Z/mvgfullLcLIf46+/1fFdkvLqU8uMZzbSia27vstArDamXk1rdh2FdnNVVR66Z1VzX9J6eKblfSOh97307aD9/J9GhTiXcRhDQLf/T8RTTak/x23ThXlIdXNR4Tk+Xyq0BFTijnU1oo/1b1BE/NeJlI2RBkuvh1uWL8UdPImsej2iy4Kt2MzfTn7IiCfVOEBv103LgfX6vpcGGyOOsdwer3R7n2plZu2JNdQdQ1uownTKFssiW5xBPh4UBFQcc9qzC4sjyEz6rx8hINSNaCq8aL3esiHozmGR0oFoXGy3YscuS5Ya1i+beAl2a//p8dfqYAACAASURBVA/gIYqL5S1PorkVrdyHMjWRaXudJb8HTeZ7NZ2m5etfJtHcSu+ffBD7+CjOgV608goie/Zn2mMvQUVNGUNn/Xl+ybP4LFHaD9/JXV/zcDbmKjKKWQRpKehPOLlroJV3NA5zQ9XqnAVMTJaDLlcmLJyKzu82jvMmOU533MlEykaLI7HAUmj1SCnpf/RUgdG91A36HjrBJW9/yZZbzjO5cJkVyrd1PoP2yJHc62ZHVZOtyiWeCO3OOD0xF6lsIMQiDMotGjevQ25yKYQQ7HzVJfQ+eJyZsWAuna7x8h1Udm69z8paxXKdlHI0+/UYpUt7HUKIJwENuF1K+cNiOwkhbgNuA/BWbPIvSwj63vPn7PiXf0KNRgGJ0A20MjeWmTBCz9SEzt521XQKZ38vO//xQ1jDIaQAhIJhs9H7/r8kWd+46OlqmrwU0x12ofNy1wRHf6Tz3fF6tGWmlaekwn+ONXB9ZYAl7EBNTFbN5d4wDwcqWNoyDkBSlq2aFgK6XHG6XPE1nT8ZiWd9k/0IAe6GCrRk8dQpPaUxerSXxss71nROE5PlkEm9kNzW+UyRKPLWu/mbmECmffXf7ujlF9NVPOCvRJOCq8pDvLZmEpe6OhOD5WJ12tj16oOkYym0ZBq717llLTyXFMtCiPuAYglafzf/GymlFEKUSkBsk1IOCyE6gAeEEM9JKbsX7iSl/FfgXwEaWvdsutdDqrqWUx++g7Kzp7CGAsSb20g2NNH25X/Gc/xYwf6KoWObnsyTDUoyQfuXPsupj/5TyYYnACPhBC1XNjD2zDgWqaEnNXSpcLUvyE1Vfh7wVyJW6DCgScFEykbDGoqmTEwW4031Y/wqULHMK1MglyWql4eWSHHih0+ipzSQGb/byLB/0WPGjg1Sf0kbimXp1R4Tk9UyK5S/8e6AmW5hct5hVSS31ExxS03x1NANP7/LhtW1NSziSrGkWJZS3lRqmxBiXAjRIKUcFUI0ABMl3mM4+3+PEOIh4FKgQCxvCRSF6K69eS+VnT1Z8pZf2OQX1FgMV183sR1dJU+jS4OXv3EXf/QXMe7/0AOMRKvYXRal1pYu+r7LwZAiF8kzMdkIKq0alZYU01rG93txJA329etINnFiJJNusYJnSKEI4v4ZymrL120cJibzmRXKH31PJ8HbP2gKZROTC5C1xrvvAd6R/fodwI8W7iCEqBBC2LNfVwMvBo6v8bybiqKtTIAqqSTlT/4mr9XTUCCW+9fvj+aqo2c+8x06bSrXVgRzQhngMm+4aFROILEKHWWBYlAx2FsWXbRfu4nJ+lDK+SL/mrQJyetriz4/r4qZ0cCizUaKIQ0D1WZdtzGYmCycy2eFcvuRu0yhbGJygbLWnOXbge8IId4J9ANvAhBCXAG8R0r5LmAv8GUhhEFGnN8upTyvxHKsbQdlvWeXvb+QksrHHkVaVMbe8JZs0Uf7vD2MEnltc1RYNd5cP8bdY/WkJUgU7EKnypbmL9t7uaO3nfGUHSMrWhyKwTsah9fwU5qYLI0hQS2ZbQWqMLAKiQK8o3GYfe5oyX1Xis3tBBFcUWTZ6rLj8BXvyHYhs1ZbTyHEDuDbQBXwFPB2KeW2z++aFcfX3jRbrW9wQ5eF9iN30feT9VtFMTEx2VqsSSxLKaeBG4u8/iTwruzXh4EDaznPuWb0DW+m465PIdKpZadHKOkUVY8+yNNXvIxrf2s/7949l/MsNW1ZeW2vrpliT1mUB/wVhDULl3ojvNgXJC0FccOSjTxnRpQwFD7R28Et1ZO4LToHPWGGEk4MYJcrtiY/WxOTWb4w2MJ0unSk9u92dONQoMWRwLIO15yhGwS6xwn0T2VNYQQraV3ZdMW2Le5bq63nHcDnpJTfFkJ8CXgn8MWNG+7WZygQAyQffW8XO5Wngcxcrh05wk++Y+G+6VqiuoUrvCGuqwxgN+dcE5MLhrVGlrcF8bYOuv/sb6j7n+/j6u/BsNmwRMIo+uIpD7pqwTfUy22daQKfyG9vutzlug5XnI4FDgI/n6gipit5aRo6Cv60lf8cbUARmaYlVgwsSkZj3NY0xNUVq28tbLI9MSScjJYxlLSjG4IjQV9uNWMhViEZTjq5qWrxorvloqc1Tt1zlGQknrOGE8rKyl597TXrMpbzkFXbeoqM194NwFvnHf8xtrFYnm1X/dH3dNJ++E4CDw/ktv1L98X8ZLI6a7slOBV18dOpav5h59kNdxMwMTHZHEyxvEwSzW30v+fPc9/X3vsDah74GVIIlBIRZyElb3/bboJ3fHVdc9mOzXhIyWLV/QIdgZ5VE2lU0tm5+ktDLTQ6krnuO8G0hW+MNPBEuBxDCi72RHhH4wh1ppOGCZng7RMhL18YbCFZtAlJIWmp0B1zclPV+oxh/LlBEuEYUp/ne16sL2sJXNWeLWtDtAmsxdazCghKKWc9+YaAUt2RLkgy4nj+tTaXl5xJt8j8OseTNu6drCE97zOSlCpTacGPJ2r43YbxzR24icl5gJ7WmDo9Sqh/GovDSs3eJjwNvnM9rEUxxfIqmbjlDQSuvo6KXz+I58Tz2MdGUPV5fq9C4Krxsj/1S/qjdaSiSQI9E+hpDW9TJWW13lU3S6i0prMthJd/vCbhZ1NVvLtlmKQh+NDZLoJpK3r2PZ6JeDh9totP7zqNz7p0y2+TCxdDwmf72ngqMtsuennXmU0YNK6j+4X/7HieUF4JQhW0X7dn3cayFdkoW09gRUtQ59QffwPIRZHfP+/60TXaD99ZkJf8VNhbdKUjLRUOh3ymWDYxWYCWSHPiR0+SjqVyBduhgSnqLm7dkp37ZjHF8iqxTk+x40ufxRIMYACKroGqoFoEQgHVrnLZdW6O3jlAoG+C0IAfhAQDxo8N4GmspPOmixDKygXzzVXT/CboI7WCbmoGCuOpTL/1w0EfM7qaE8oAEkHSUPj5dBW/W29O8NuZH47XcDTiZaUGhoqQXF+59g6SiVCMsWcHSM0kVnysUAXueh/Nh7pwVrrXPJatzAbaen4P8AkhLNnocjNQsnr4XPvjryc5ofzeLtof+WzetmIFfIooHbIwe0OZmBQy+kw/6Wgyb5XQ0AzGnh2gelcDNrfjHI6uNKZYXg1SsuOLn8E6NYEiJbMJEYqQuOsrqdnTiM3t4IkfPI2h6XOteLPXhqEZREb8TJ4aoXbvylc3u1xx3tYwwjdHG1GFJGWIbB5p6enZIjLWcpDJQU0ahWkcmlQ4MeMGTLG8XemPO/jvifoVNhPJ7P0nLQNrti6MTc9w6sdHMXS9tOuFAMVqQWp6flqGgOYXdVG7r3lNY7hAmLX1vJ1FbD2BmJQyOc/W81PZSPSDwG+TccQoevxWQaTTuE+9gJJOMdO1B92TXRGREvvoMMIwSDQ2g7J4Ss6sUL72xtaiUeRiXOEN862RhoLXrcLguor1yd03MbmQCPRMlEynCw5MU7tva2Z8mWJ5Fbj6urGEgygLqvINTRIZDlCzt5HeB19AS6RLvENGME+dGF6VWAZ4ebWfa3whvj9ewy+mq5fYW2LFoMMVYzBup8qSxoJRpJW2JKZv2xzPbYUhYThpxyIk9bZUrtnkf4w0rrBvJIBAweBEtIwryiOrGo+e0ggP+Rl+qgdjSV9zgcVuIZVakC4kYfjxbpwVZXgaKlY1jguItdp6/hXwbSHEPwBPA1/Z7B9gOZSdOk7bV/8l55AidJ2Jm29hZs9+Wr/2RdRoFIRAWiwMve2dRPZfUvR95gvl27qe5ezXl5dO9GhgtuB19lMjsAidRnuKV5+jbmgmJlsZsYjEWGVm6qZgiuVVYAkFMRAUK7EzNJ2e+56fiyYvgp5eWxTOrhg86K9CX7S3jMSCJCFVPt3XDoBSMt9ZMJq00x935AoBTS48ngl7+NJQMwlDwTAEZRadW+vGeFlFgJPRMhZfQJZFt+sonIquLu1h6vQoA78+jVAExnI+E1KSihS/Pg3NYPy5wW0vltdq6yml7AEObeQY14oanaHt3+5ETeUXJdfcdy+1992Lkp4XrEhC679/kbMf+DDJhvwAxUKhHLzjbkrXQ85xLOLmhxN1RdxhBB9o68NhWseZmBRQ2VXP+HODRRtM+dqWCvydO8ww4jKZ37Wp11uXyVEuwXKEslAEvra12VqdjbmWzFsWgI6Y58ksMAr6/81hIDgc3NpVqSarZzBh53P9bYQ0K0lDJY1CULPwleFm3ndiL2rJK0MCErswKGbeJpDU2FZe3BcPROl/9BRSN5YnlJdBsoSQNrmw8D31GMXKFtV0GqEVmZ91jeqHfpE3lxcTyst1LvrpVHVJp5jDIXMONdlaGBImU1bCWrEw3+ZRf0krDq8TxZL97AgQqkLzoU6sLvs5HdtimJHlZZCbUHNdm9qoHngxoXsfwUitwkdTgGq3Un9J65rGNZmylvS8BYlT6KRkfiFf3iCKYADaCgoHTc4v7p2sQSsQGJm/d1i3YhEGijQwFjxH2zD4x12ncamSPzu5m+QC60KrkNyyimXn8ecGMrP4eiEEnvry9Xs/ky2LGgkh0sWtLkWRxjWKYWAfH10wlwNSX7FQhoz9ZjE0qXDvZA0qkpurp83mJCbnnCdDXr4y3ERMVzGATmeM97UMEtYt9MRcVFrTHPSGsWzCrV+1Wtjz+isI9k4SGpxCdVip2d245QuyTbG8BPMjD/O78CXfYuc3fS2MPz+0ooiYxWWjqquOugOtWJ22NY0tvYioVYAPdfbw8e5O9KKezFBsSd2uGFxZbjYvuVAZSjgKhPB8LELiVHWimkoKFbKPY3/QNEyjI7Os/cEdffxzfxt69vozgD9sHKZrQfOc5TAzEV7FT1EaxaJQd/HaHkJNzg/i7V0YdjtqMn9FQwJSUVCM/ECGoaqM1zUXzOVaLMHQX36LiFGPdQWd0S/yzDCUdKAViS5HdAvfHa/nwUAlPkuaU9EyrIrkWl+ANzeMmc1KTDaNM1EXdw20ZpvmZF+LufjA6d0oUiIFqCJz7/9wRw9NjrXZfxq6QbBvkpmxIFaXnapdDdjK8iPGiqpQ2VVHZdf5YzVpiuVFWLhEN78L31S0jsbLO2i8vIPj33+CuH+m4HihCNwNFaQicZyVZdQfbKes2rNu46uwatiELJqKUWFNU29PLRJ5zmBDz4oisAudSz0RdrtiAHTHnDwT9mBRJFeVh8yGJecxSUPQHXNRaU3RE3eWdLvQJbzYF+DeqVoysiOT4f6vwy1EDQuvqZlivzvKl/Yd53S0DE0KdpVFVxQ9MzQdKUG1qmuKKitWFW9TJaGBKaSUuOt8tF6zE7vHuer3NDl/iOy9iFRNHfaxEZRs2oUUAsOWuTHLZCLvKjcUlep3v54/njeX9z/r58zhCaQUSKOXsppyWq7eSWTETzqextPow9tUWdQT/9XVUzzor0TXRdHPU0oqjCTtjCTtgEA34MFAJadjZXxy5xlW4RpqYrJifjBRW6ARDBQMmZnfkZCWkDAUbu/dwZ17Tq660E5Lpjl5z1OkoykMTUcogrFn++m4YT/lrVs3H3k5mGK5BAXV0R95iFJFHy1XdXHm58fyEtYVi0LVrgZar9m1YWO82BPBoeik9HzbOJsweF3NBC7V4GUVfn7pr6J42oXktbWTPD/jxqZIbqj0cygbVf7CQDOPhXykpUBB8r3xOn63fmxVS+0m55b7pyv4xmgjCpkUm8XkqZSChwKV2X1mr5nM/98abeDmqinsSiYSsdcdXdE4EqEY/Y+cZGY8E00uq/FgddtJhktHpIWqoFpU6g+2MXlimFQ0E/Wwe53seOk+XFVu5KwTwlYupTZZfxSFnv/1V9Td+318TxxB0dLM7NpH8LJDNP/X1/J2lYCaTlF+21/wE5+V5hcfIBmJ0/foRF6NycxYkBM/eAKhCqQumTw+hKPCze5bDqJY8lfoKqwan9x5hv8abeDxkLfEik3+NalJhfGUjWMRDwe9q3OOMTFZCUMJB8Xv/4WFqTO6SnfcuapVQsi4EaUiiZw1nDQk0pD0PHCcS37vxQWfofMJUyyXYCXV0Z7GCna96hKGn+ghNj2DxWml7uJWavY0bugYLSKTavHJng4ShoIgI4auqwhwU1XG4/NtDSNZsVwMQbU1zUc7e3jQX8F3xur4t+EmKq1pxpL2XAvX2Rbad4/Vc9ATWfMyjcnmcXymjK+PNOUtwS2GTTGY0S2Uyml/yF/FK6qnVzyO2YiDnpwrvIpOhJfs3GB12rjoTVchFEHtRc2ko0kQIm9ZzxTJ2xfD4WT01rcxeuvbcq91fvrjKFq+befsFSLTBpHJJKfufRqry1ayGHu2c6ShGcT9M4w+3UfTlZ0F+9Xa0vxp2wBfHmzm4UDFsvzJE4bC2ZjLFMsmm0KTI8Fk2spy2uQIYEZfXNDqaZ3JE8P4z44jFEH17gaqdzcgFAV/KQ9lAeHhwJZ2u1gKUywXod8/s+LqaHe9j92vvWwTRpdPiyPJv+w9wcloGWHNwk5XjCrb3I1iRrcuevzRiIeBpIMHpitzRVtRXaXYB0uTggf8lby9cXRdfwaTjePHkzUr6PQo2eOa4ehM6QK5pLE6A52pU6PFhckSWRjpWBI9pWFxWBFCbNnuTiZbBClxDvYvvZtulLQfLLbv9JmxomJ5lpdW+jkS9JFcxmfNJgx81tIe/CYm68kbaic4PuNecB8obgGqSUGns3RU2dB0Tt7zFMlwPLeSPhSMEuidZOerLinZbARAGud3nr5pHQf0+6N5/1ZjI7QcDE0nPOQnPORfRuOF5aMI2OeOcpUvlCeUAazKYlnLgpGEnfumqxa4GxQ/QgI/m6rmyZB3HUZtshlMpGwst/GuXRi8ssaPSyl9bV6/yq5ksclwUV/N5SBUM3Jssjz6AzF069KF09KQiBUkDS9lB7q7LMYrqyexCQMViYLMOMtQeJwi4GpfMP/9Zcb//EcTNfw64CNlmNe8yfqwqyzG+1oG8KoadqFjFQaN9iRWDOZHK+xC55aaSTyLdGGdOj1KKhLPm8sNzSA6ESY85Ke8ubLocdKQeBrPb+/7bR9Z7vdHufamVm7YM1cU1JU+su5C2d87Qf/DJ+etB0Lbdbup7KjLPHEJsSHLyV6LTpU1zVTRZRiJx6Lj1yTpZdVZCQzgroFWPr/3xKIfKpNzw4ymEtIs1NhS2BRJlyvGWNK2iAPG7I0dbq6a5oBnhve2DPKZ/vbs9rkL9lpfgHLr6v7m9nIXQhFFIw+z+aHF8DRUoFq3/TRlsgz6/VFA0vAHr2Lq6z9FJhcpSBbgbvAxMxLEWOohTkB5a6lUtjne3DDOtRVBHg+VI4Ery0M8GfLyg4k6LFlDaEVI/k97H2Xz3DAimsrHuzuZTltJGQo2xeBrI418pLObFjPlzWQdeJEvzJXlxxlP2bArBpVWjbMxJ98Zq6c37sRnSfO62klesuAhbiGBnsmiD46GphPom6T5RV1ERoOZIu7sXK9YFBov78BiX3yVe6uzre9Cs0L5ts5n0B45kns9+PDAmoRyeMjP+PODpGNJPI2V+Nqq6X/4RMFF1vvgcQYPn0FLpHO+y3UHWtZdNP9pWx8fPruz4HUrkmsrAvSNlnIPKL5UI5A8FirP5UWbbB5pQ3Bsxk1MV9lbFqU6u5KQMAT/OtjMk+FyVCGRwKurJ3ld9SSPhcpJltADAnhj3TjX+EI0ZN1OriiP8H87z/KV4SZGkw7KVJ031o2v6e9ds6cx46lcYhTOajfxqbkcTqEqWBxW2q7bs+pzmmwfhgIxQPLR93TS/NCP+fXhMgJndQzDQBaJBCiqQstVO5kZDzH8eHemm6qUWMvspOMpZHauFopAsao0Xt6xrHE0OZK8wTGR+77FMclNVX5ORsuwKwb73DMFXrZfGW5iLGnLdWJNGCog+XRfO/+8+9SWbgFscv6gCHJzPECXK87fdvSu6D1Ua+l8ZtWiYvc62f/bhxh/fojISABbmZ3ai5oviI6q20osZyIP85Hc1vlMkSjy6oXy6DN9jD3TnxPGiWCMieNDUMQkHwlaIiN29GSa0aO9aIk0zYdK58athi5Xgr/d0cPn+ttIGQqKABXJHzcPcZUvxPcm6kgYCvnCuLhQhkxeU2yJIgCT9edU1MWnendke+mBLgU3VE7zjsZRPj/QyrGIh7RUcqsEP5mswaFIPtzRwxcGW7IWVpC3vAEcn/FwfUV+RGFnWZzbd51dt7Hb3A6aruhg6LHugm1SN7C7Hex69SUEeiZIhhO4qtz42mtQVDNTzKSQjFvR/Dk1I5Tbj9xF3/0abVcfpLorTGwygpZOM3l8BD2ZBiFQVIUdL92Ho9yFo9xFVVc9qWgS1aai2iyEBqYYf34ILZ7C21xF/cUta+os5rHoXFle3E9cMwRPhb05oTyHIJS2MJBw0OYsnlsd1RW+P17L4WAFEri6PMitdRO4zRW/bUF/3MGJaBllqs7l3vCmeHdX72kgMhooCPwJVaFqZz0AVpd93TXMVmDbiOXZJbqPvn9epEpLE7z9g+uWbpGOpxh9uj8vn2exhPeFGJrBxAtDNBxsQ7Wt75/mgCfKv+0/Tm/cmUvit2S9cf96Ry//0N1JWgo0Q5DOtcYujkVI9rszvtIJXeF4tAwB7HPPYBWS8ZQNi5DU2MwiluUgZcYk/oUZN05V56ryED5rfrvehK5wR+8O4kb+Q8qD05XUWFM5oTyfpFS5Z7KG19RM8pndpzkbdfKtkXpOxt3Mtj6XwMmoiw+f7eL/23MS2wZ2G5Ozz19FThGdCGOxWanZ07Rh5ze5MJi19Vw4l7cfuYu+n8ylLZTVeCmrydRX1F/cRiIQRUpwVpblrd4JRWD3zBWO+tpq8LXVbPwPAmiAUaIoUADxEgW1KUPwoTM7mUpbc01R7vNXcTTi5Y6dZ3CYTU8uWAwJdw608nTYi0HmfvyV4Sbe1zxImUXHa9E2LH2nvLWaio46Aj3jGcEsQCgK9Ze04lrHHhJbkW0hlnNC+T2dtD/y2bxtfeuYlxwZCWTyMtfwYC8UQTIc35ALTxHQWcQ/scWR5F/2HefZiIdjEQ+/mF4sP0/S7EjQ6YrzK7+Prww3o2bz8TQDrIrMevkK6mwp/ndbPy2OJAlD8FzEgyYFB9wzZvQjy0jCxmf62xlP2jAQWITkv0YbeG/LIFf55jopPhH2FjWOSKHyzbFGSsX5E7pKylBwqAZdZXFq7GlOx2VesxoDhYSRSa25tmLxnLW1YHPZUVS1aHGrtWxt3SxNtgc5oVxsLv9JaYEghNiS7XQdiqTRnmQoWejykpAKfXEHe8piBdt+HfQRSFvyugdqUiGUtvCrQAUvX4W9o8n5wc+nqng67M3ZgWrZG8PnBtpwCAMDQb09yQfb+3JpeuuFEIL26/ZQs7eRYP8UQhFUdtTi8JWt63m2Ihe8WM4TygsiD+uNYln7krE0DKyuzRUOPTEn3x6rpzvmwqHopYJ/WQRDCQc9MQdfGW7OfGDn7ZyeF9AYStr52NlO3tE4wleHmxFZUa1LwVvqx3jVNm9w8j8T1fzXWH1WuGbE62wL8y8MtrDPPYM3+1AR1iykS1bIC/QSfzED+FxfK7fWT7CrLEZv3FW02C9hqAzEHbAOqWXRqQjDj58lOhFGtVmo3d9M3YEWfO3VDBw+XbC/YlGov7ht7Sc2uaDJCeX3dtF++M41z+V6SsPQdCxO2zn16v7DpmHu6N2RtfaaPw7Bf4024FQMrq/Mf4h9JuxZ4GCUISlVno54TLF8AfOL6eoSvvmCRPaaGErY+URPB5/doJz3+as224ULWixvplAG8DZVLp52Iea+sLpsaPFU3v5CEXgaKrC67CQjcUIDmQnP11a9Yf6yZ2NO/r67MzdRx7LFJYvlLOsIvjdel3uiLY1Ak4IvDzVnBNq8/e8eq6fDFWN3kajJhcpwws6PJ2vojzuotKY5FvGUdKlQkDw+r4hylyuGRUhKmEYwl9+Q/zeTCI5FPZzscfOnbf002hMMJ+0FzRPsQqd+HdqZx6YinP6fo7mcNkNLMXK0j9h0hI4bLmLXqw9y9ufH0LPRZUM3cNV6GTxymr6Hj2Mts1PeXEnN3mYcPteax2NyYTC/o+pahXI6lqT34RPMjAZBiEwh6Yt3nbN2vPvcUf53Wx+f6dtR8MibkirfHa8vEMtei4aCUTB/CCTllvwULpMLi9gyvO4NFIKahVMxV9GVCZOVc8GK5fnV0ZshlAEUi0pZrTczCZfY3nnTRXgaK5C6Qc+DxwkP+XOWWo5yF4pV5blvHyEVTWTygYTC0OPdNF7WTv0l6x99++ZIQ5Gn1FnhVVwwq0hCmnURO7I5UnJWfOeTlIKfTVWzu6yUQ8KFxQszZfxTbztpKTBQ6E/IRftxaFJkiy4z7HDGsCkGSX1hIeZ85q8J5EeoUlLw1eEm/qy1n2ci3gKDeh3BAffaO4oNPdFdUPwhdYNg/zSJYAxXtYcDb72G6HiIdCLN4JEzzIzMfV6SoTgToWEmToxQf6Bl0UYQJtuD+UL5tq5nOfv11c/l0pCc/PFRUjOJ7EdFko4m6XngBXa++iDu2tINeTYSgcChGAU1CQDTaSuGzKTRzXJjlZ9fBSpJLZhErEJyU5UZVb6QOeCe4UiwfFn33+mUDUyxvC5cMGI5I47nyOW1bZJQhsyyXnQiVHJ7JnLsQwiBsKh03XyAZCROIhgjOhlm/NkB4v6ZuQMkyKyp/cjRPjyNFeu+9NEdLxW9Kx1ZVpBc7g0xkHAUFJUtRCBLtIAV+NPnt+/icpESvjzYkrdsulRbXFVkJsVZ7pmsyd5IFztukQo6MqkcVbY072ke4PODbdkrK1voJyX/t6eTT+06veqq6lQ0SXS8+PUvBETGgoRHA0weH8ZIZ5bAtUSJKJghmXhhCG9LFZ5636rGY3J+Umwun98oai1uRaHB6YwD0YKPiKEZjB7tY+crL1nW+xiaTmQsCDLTvXUxS63lSOKKOQAAIABJREFUUGdLoZco9POqOgv7p7Q7E7y1YYRvjTaiCAkSDARvqh+jq0hdismFw+/UjXM07CVhlLq3ZjCkoG2RbnwmK+OCEMu5yMNNO3Kv3dClbKpQBkjHUghFQerFi9dar9mFULJJ+Yk00pDY3A5Um4Xu+55ftMOZNAymTo3kiWVD0wkN+tFTGu76chzlK1+2dioGEb14/lNxYSZ5S+MYV5eH+NlUDWl9MecMiVVkMnIX5tdZhcH+shkmU1Z0mSkGvFD9RP1pK0Ft+R81VRhcWR7KWUbdN13Jd8frWboTn1jwfz4SsCsGipL5/c//m+goRDSV+6creW3t4rnksekZIiMBVKuKr70GQ9fpuf8FYtORks1FDM1g4NFTi2n5osdMnRwxxfI2othcjtTXraNqIhgt2T01HlhoLVqcYP8UvQ8dnzc+SeuLd+ess1ZDkyNJuzNOd8yZZyNnFzqvqx2nN+bk18FyDASHykPsdsV4RbWfq3whjoYz94RLPZECFx2TC486e4p/3HmG/x6v5bkZD6qQBNOWvEizVRjsdc/QbDa1WTfOe7E8v+hjp/J07vX0o49sqlAGsLntxf2UyRQx+dqqSYRinP3FMZKhzBOfxWGlem/T0i4aEoL907jrx6jYUUtsMsyZnx/LbpNICZUdtbRdt2dFxSo3Vk3zk8maBakYpdWMXRh0OuMkDYV97hmeCHnJDLv4OS9xh3ky4iM/Up3pGnc45OPeqRpmOwm+r2WQfe7l3bC2AqNJG/1xJ1XWNF2uWJ7YTxuC34TKeSLkxSIkpVPZ838vAM32BK+qzgjW01EX3xhpZGmhvBSS3a4oLtXg2RLFQSmp8lS4vKRYllLS+9Bxgv1TkG0XPHDkDIpFQU/pJa/9BcNYEXradE3ZTsxGkd+9+1juNalp69ZR1e51olhUjCLXld1bqjnTHMlwnJ4HXigIbPQ/egpnZRmuqtW7GH2wvY87B1o5GS3DIjKuQq+oniaQtvKxrLUnwAP+Si73hHl/6yDlFp2XVQZWfU6T85M6e4o/aR3Kff9kyMs3RhuYytq2Xl/p5/caRs/hCC88zmuxvLDoI/DwXP7reraqXi6KRaV2fzPjLwzlOkBlXleovagFw5Ac/97jeUV9WiLN2NN9iGU0X9DiKfofOcXo032kosm8c0Cmpbar1kvt3uV71d5aO0F/3MkLM25AogiwILGpBv50oSuHIsCravzNmZ1EdTXPgqwYT0bKiywVCZJSYTxlY1YETqdVPtXbzid2nsGmSMpUfVNM1ldD2hDcOdDKsxFPRggDlZY0f9fRS5UtTcIQfOxsF2MpG0lDzboZQ2Fqi8wGWmdfz2zrTzj5eHcn72wa4umC/OLVM+uN7bboqNk85XwknkWKg6ZOjRLsn8pdd7PXsb5Uu+BVolgUKto3x+/W5NzT74/m0i0Cn7g7b9t6zeflrdWoRcSyYlFouLR9yeMnT44gizwUSsNg4oVh2tfQcdJt0fnbjl78aQuBtJUGe5KRpJ2/7+7IC2YkDZXfhHyIQcnbG0cpN204tz1XlIe5ojxMysjYjy5M2zFZO+etWC5e9LH5AnkhjVd0IFSF8ecGkbqBYlGoO9BK/cE2hh/vLumWsVgKxsL9ZqPSBds0g4nnh1Ykli2K5C939NEfd3A25qLconHQG+bEjJtP97Xn2RnZhMHvN47w0+kqYrqyqFBWstnWctEihPzjU1Lhr0/vQhUZIXeZN8y7m4e2nGi+e6yeZxd0yxtPCe7oa+dTu87w08lqRpL2XD63nBc5tqKTRs1Wss/K6MKHibQU/PtwM432RJHtq2GuYPD6igA/n6ouyJG0C4OXL1IcNHl8qOABbcNQBPZyFxUdtZtzPpNNZ2FH1Wtvai3RUXX9UFSF3a+9jO77nycRjCEUgRCC5qu68DYu7ZuYmolTdJlIZretA5VWjcpsOsUjgYqidSEGgsPBCp4Kl/Ohjp6i/vkm24+NbCq13TkvxXKBUP7IQ+d6SDmEEDRetoOGg+3oKQ3VZkFkH/NCQ/7SxykCRKbQCkNmKqKWs6y9AD25OhPyNmcir63qAc8MH+3s5gcTtfTFndTZkry+boKL3FE+cGpXkfasABKLyEhDBVl0qX+OQgEoEWiInCXd0bCXO3raeUPdJCHNQpcrRtM5zsGSEn45XVVwAzNQGE/aGYg7St7gBOBUDQxdzIvqLvLAISQ1thQDCUeJ33fB6ChlIedQdHZmb6hNjiS/3zjMf4w0oWQLMCXwqpopDnhmFr5pDj21sgiWUEXJHOb8Hcnm+hsoNgtWp5XqXQ3U7m82211foPT7o1x7Uys37JlLfehKH9lQoTyL3etk3xuuJBmJo6d0nBWuXC3JUrjrfYQGpou2+/U0rINJ+QJShlKyiEsiSBgqdw608s8b5Ke7XkynrDwd8aAAl3nDZm51FikzaTU/nKgloFmos6V4c/1YyfboJueO81Isr2d19EYhlIx/53ws9tLuD6rdwu7XXMbUqRFS0STexkqS4RhjxwZWlOfpaVi/YqgOV5wPtPcXnkMtPtFZkNxY6ef1tRPc3ruD/sTSOYCLoUmF0/Ey/rnfCQKkFFzsifCnbf1YztGNIWWIkmkRmhR8bbiBGb34x0oCYV2FZQnfDIfKQzw/4yZmLNWC3KDFnkAVkt6EM+vHLHLbqq1pLvPOTcA3VgW43BvhqbAXXQou9YaXbE9e3lLJ5MnRZT3ECVWhZm8jUydHCoQF2aEpqoK3uZL6S9rQkxquak/BZ8bkwmPW//62zmfQHjmSez348MCmps/ZPSufn6p21jP2TD+GnspzaVQsmet9vXlReYgjoXKSRSzlZgmmrYylbDSU8ElPGoJnwl4ShsJ+98y6d3Vbih+M1/CDiTpEVvZ/baSRtzeOcHNV6eDRduF747X8z2RNLrA0knTw+YFW3tU8tKHdVE1WznkhlvOX62SeUD4XucmrpeHSNs78tPgHoHZ/M45yF82HunKvxaZnGH9+aNkpGgD1l7SueZxL8arqafoGnQWRY0XA62sn8Fk1bq6a5hsjDUtEl0vb080nKdXcjelYxMMPxuv4nfrxgv3CWuZc3g3M4TsTKytp6GAAJ2KzLXWL/WyLC95iHCoP0+GK8/8PNXMimt9SVEAmPw1JpyvOX+3oxaZIhhN2vjHSwPNRN1YhebEvwFsbxgry2HxWjRtXcMOqP9iGv2cCPaUt/gAnoP36PVTsqEUoChMvDGVWTqTE4rRRtbMexaLibazYkLbuJluX+Y2igrd/cMH8vfXnctVqYc9vXcHQY2cI9mUKYb3NlbRcvROLY/07r17sibC3LMrxmbKsZ30hQkiMEg/wxyJuPtef8eeXZOzEbq6a5vcaRjclEn0y6uJHE3UFK23fHGlkT1mUlm3s1pDQFX48WVvQ5yAlFb412sCLfUEz93gLseXFcsFyna7RZTxx3gllyHT4q+isI9CdL/Rc1Z6i7X5dVW4qdtTiPzu27HP0PnSCfbce2tD2rYfKQ5yMurjfX5VJuchO1n/SMpBbXntZpZ9jM26eDXtJyuIpB3PFbYXb8veaIyUVfjldlSeW++IOvjjYwkjSDkCTPcl7mgcZStq5e6yeQNqKUzV4RdUUr6udzMvrShuCH07Ucp+/kqSusrssylsbRvNSUuYTNxSswihx41qv37nEJiTvbx3Aqkga7Ck+0tmDLuGZiIeplI0mR5yoZiGoWelyxfJyFpscSf66o2+dxjKHrcxB180H6P/1KRLBWGnBLCE+PYOvtZrmQ53UX9xKbCqCxWHFUVmGFkshFIHVZV/3MZpsXQo6qp5n8/cstjI7HTdctCnnUgT8n/Y+Dgd9/NtQU9F5x6XoNNgLRWdEU/lMX3uBGLt/upIuV4yrfaV7AqwXv5iqmjf/z6FJwYP+Sn6/cfs6Nvy/9t47SpKsPPD93Yh0VZlZmVne+/bd02N6PMNYjIYRM0iYEUggCS0Lq913zursO4LDPrGSVu8hHWl5eusQiwy77AoJ2BENDIyBGQYYw8z0THtXXdXV5W1mVXoX9/0RWdlZlaayvOn7O6e6MyNuRHxxI+PGF9/9zEjcjp7Jkb2UcFonlNY31PCjWBnbWlk2o6Pbtny6bj3pfvAgwQPNTJ0fQaYMavc1U9VWXVC5TcWTzF0rnfN2KbFAhPmRWTytNeslch5CwCdaxnhv7QynQi4cmsFtVfOLAvE0Af+64xpXIhWcDrk4F3JyLuTKRuoK4LG6Sb4zWV/C+lzY8hxO68ynzIHEn7Twh1d6MsFrC9kkHHy+rzcTgGguC6U1vj3ZwMmgmz/svZJ9Y/+Lqx2cC7uylo9TIRcXr/Twx719Ba0ehmTZQixrpcka53M9A3luEbqA26rWXmVvJUgpCU/NE5+Lkk6mGflFH4ZReIDPZeLMEMHxOfY9dgsWh5Wq1mqC4wHOfvM1kpEESInD56TrgYNU+Jyld6bY8eQpypuc1nMnowu4zxegyR7nT/q7SRmCFBo6Eosw+EzbcEEL5KtznpxMPNeJS50fTNVuirI8n7JQaAw3EARXkHt+N1JlSZEqMiMgAIe2vQLbb3S27a81kTJKREfvTEV5AXejt6xCCzOXxkkXSaBfisDV6Q1VlhdosCd4l730NH5PZZSeyihP1E8RSFo4H3bi0AyOuEJ8e2I5Rbn4mn95/gDv9Pmp0lMk8/x5RU51OhYtvxKt5FTIxc3uEP2RCs7nKMoLbRKGxjfHG/i9zsWluF8LVPFfhtqXrb63WNKVWZstGNzuXd5/eDNIxRJcevok8flIYb/jEsi0JDoTIjQWwN3sM/OL//Dkov1EZ0Jc/O4JDn/krpL+/Iqdx9IqfEpRXju9lVH+bO8lfjhdy0C0glaHmY+9mK9yMGXJsyovMF8krmK9ubVqnssRZ54cdpHmqHtzX/y3G3W2JF0VUfoilYsyS1mFwd3egMpssc3Ytsqyq8q24WmEtjuRmWDhNEXLsNbSqxuF15paZM2otiWzqdTyKV7qTWZSq70468ukmStWgbDwtifn3dzsDnEpUkkhFVAiuLjEP1hK+NpoS5GHTzGl2DwHHSMj4/KKsybMQjHbgf4XzplVzVaRlQXMCpOhiTnczT4mTg9hFPC9l4bBzOVxGg63rVVcxTYhvwqfwUO9FqUorwN1tiS/UcJ1IZjSeWHWR3+0ErtIF3QZ05Ecdm2OovpgtZ8fTNcRSFpIZcZpizCotyW4y7Pxlu3NQkq4GKnkbMhFpZ7mbs/cshk/xuI2oukFw445xlqEZJ8zzG81j264zIqVsW2V5Tp75IZWlAFzenqlKeQ0QXXv6suubib3eAN8fbSphBG5tHKZRiO9YkXueubnKksqUykrv5V7ia/YXMpCKF0kwIbcwiL5GCWzX0isYiHlnuRftQ9Rvw2syslogtBYYNWKMpgZAiwVZtBTdDZU8DobKcNcp1hXhBDVwD8AncBV4MNSSv+SNg8CX8pZtB94Ukr5T0KIvwPuBxY0mt+UUr693HETKSOvoqpMpUi98opSlDeY4ZidL/T1kJIaCalhEQZpKXJe1s05N7smebx+alNkqtAN/u89l3lqop5X5rxoQnKTK0hSCv5koIt9lWHeUzuDbwenkktJ+POrnVwIO0kYGhYh+cZYE59pG+KuIq4ucUPwhb7ezDMl57khJU82juPYZrUFFNtYWU6N+29oRRmgdl8To29dRRbS5gqhCZpv6aSyxrV82y0mZQhGYg4+3DDO18fXUs555ds9lMkAcVvVPP+twHqrMHi0dvHDxKGni+r0upBYZJoYhVwJCnkNXsciJL9UO80tVfP0Vka2LCXeUtLxpJn72FhbgImvy6zAV+FzEp6az1OYha4pn+WN4bPAj6SUXxRCfDbz/fdzG0gpXwBuhqxy3Qc8m9Pk/5RSfmulB/7Cp3u2RUXVG40vD7URMa4rX6nMLJhNM7CJJAmpcZM7yJON40XdvJKG4GzYSdLQOOAM41qHADO3Jc3HW8b4eMsYr/g9fHm4jaQ0zRZ9kUqem6nlj3r7tjyH/mp5drqG8znZShbKkv/XoTYOuUJ5hheAVwNeElLkufSlERyfrONfL3EBVGw921dZNrataJuGxWFj/2O3cumHJ0nHlrc2ettrSpZsTcWTzA/PIqXE01q9IamOyuGVgIevDrciMXUns/Qy5Cu+K/f5LY3kYGUoOyhLTEV36XFSUvCLOQ/PzdTQUxnhsbppGu0JbnEHOTHvXuL2IUlJQbrorVT6HAwpeHfNDDWbbE1OxRKMnriKf2ASEFT3NNB8aye6zTwPm7tiTV0vdEHvu2/K+iI3HGlj9spEnu+zpgtq9jSt/kCKYjwOPJD5/DXgRZYoy0v4IPADKeVSZ+MV4aqy5bhbKAV5swildAZjDgoH08G/7emndRll9FTQxf87eD0rU0oKnmwc59G6lQWZFyNhCL4y0rrIlS0lNVJS8jcjLfxfPf3rcpzN5vmZmoJZSgSSX8x5CqbnHI3bCubOlgiG4441yRMPRhl9c4D54Vk0q07d/mYajrSVVXjHSKWZuTyOf2AK3aZTu7+ZqpbCCQhuNJRGus2prHVz9GP3EhyZ5fIPT5VsOzc0SyqWLFjYYfriGNdevpT90Utp0HpHD/WHNtdXdCDq4MtDbQV8fxeUVgCBhoGOJFmmr28hRNY5wtzvPZ4An24bya5/JeDNRCMv3r8ETobcgOBarIKfBXz82+5+Pt40ypvz+1msAIvsNvksNyMgucsT2HRFOZ1Icf6f3iQRiWd94qfODTM3NM3BD9yOZtGRaQOb026miCuCbtOLVvVzNXhw55QPdnid9LzrCFdfukA6nkRKsLsddD14SBUi2RgapJQLzq3jLK+5Pgn8hyXL/kQI8QfAj4DPSikLaltCiE8BnwJoq3Mrd4stYPmRpjSBpIX/cLUjL+D6H8Yb6ayIctAVLrJl+fRFKouM5ILzYSdpaWb+2GnEiwRRphHEjcLrWh1x7CKd198CSbujcNrSckiEYpx/6g3SyUwu/FiS0RNXCY4F6H3PTSWV3nQyzYXjbxIPRpEZo8b88Cw1e5tov2fvqmXaLShleQcghKCqtYbqngb8A5PIIkF/QhPEQ7Gs8iENg/DkPPFglGsvX0KmjUWD5vAv+nHWeXDWV2Gk0iwogZpl4wIEn56qy05T5WIVkrs9fmKGjlWTvMPnp7ciwh/09TKVtOWk2FnO+mwW6dAF3OUJ8PHmUfwpKzXW5KLUdgCDUUeRyljX92cgiBs6/224lXfVzGAVxjKFVq7L0WyPMZWwFzxfkDTa4vxu21AZ+1pfpi+NkYwmFgWPSkOSDMeZ7Z+kdm8TV39ynvh8tOD2QtfouG8fM5fHCY74C7aJzOT7IVe1VHPkybuJz0cRmobdvTYLyo2OEOJ5oFCAwudzv0gppRCieGSAEE3AEeCZnMWfw1SybcBXMK3Sf1RoeynlVzJtOFxXp0L4twC3JU2LPV6wamqFZtBSIA9zLj/1exdlZFggIQXfn65dsbIcSWv4k+a4u+B/qxX/CZYs15QyBK/PVzEUc9BgS3CXN4B9G2WKuM09z49nq/MCzTXgSJGMH3d65vifY00kUiInisZ8Dr6/fnLVsoy9dfW6opxBpg1C4wHCU/O46j1Ft506N0x8PrqoCJqRMpi+OEbd/mYqqre/e+dGopTlHUT7vXuJB6OEJwvXjZeGxO4yCz3MDc3Q/+OzpoJcRLmWaYPREwMkI3GiOVUSK6qddNy3H2dd1bqfw1jcXjD1WlJq6II8X60/33eJ0yEXVyKVnJh3MxR1kMhkz7AIA5sw8FlTTCRseCwpbnHP01MRZb8rTGMmpZLLcv1BYUizsEiFZtBoS2ARRta3rxQjMQdXwvlVC4uhA3/ce4WvjzbxcsCbs5159o9UT/PR5nHKmBlbdwKD0wWrQhopg/mhGTxtNcwNzxT83QhNcOhDd2J3OUhFE0WV5XSicMCOEAKHp3JtJ6AAQEr5SLF1QogJIUSTlHIsowyXegJ/GHhKSpmd4sixSseFEH8L/Jt1EfoGJTobYvzUNaKzIRw+J403dax7bMk/bx3mj/q7SUlBSmroGOgC/kXb0LKV4GaS1iL54wUzifLd9VKG4G9Hm/mp38xUZEjBw5mKgXsqIwUVZg3Jze75gjLOJi38QV8v4bROzNBxaGm+PtbEF3quLOtWUi5Swsmgm5/4vaSkxr3eALd75sq2cj/RMMlrc17CabIKs11Lc6dnrmiFQpsm+cOeK/yna21cjVWgAS5Lin/WOkxXkYJY5TA/Mls4kDptEBoLlFSWZ/omCj4XpGEQGJxWyvJWC6AoH91mYf/7b2PgJ+eZ7ZtYlKlA6Bq+zlosDpuZ0/bZU8vPvVH45orOhrn4vRMc/JU71l2x2VMZZjDqyKYRWsCupemtzLdkagKOukMcdYd4on6Sn/p9PDtTQzStcZtnnsfqpvCUEYQiJXxvqpbvTNUTNzSsQuLWk0WTwhei1RHDrqWLWKOvI5B0V0ao1A3+WesId3jm+fGsj7ihc7c3wL3eANZ1tIxIKYnMhEgnUjhr3Vm/40Kk4knCk0VSNgmwVNhIRhJmcF86v1+FJrKvOp72WoZf7y+Y3vBGH1i3AceBTwBfzPz/nRJtfw3TkpwlR9EWwBPAmY0SdLczPzLLledOm6kTJUT9YQJXp+l55DCetvXLh99VGeXP917i2Zka+qMVtNpjvKd2Jms0KMV+Z4Sf+NPEjKVp5gwOucrPVvPXI828HPCSlBrJzLDwo5lqdCS/2jDJu2tmOD5ZhwBSaNhFmgrd4LdbFqdKk9JMBPXloTb8SWvW6m3KJ/nSYAd/se9S2XLlMpu08OKsj6mEnX2VYU6HXLwZrMqO66dDLp6fqeZz3QNlKczV1hR/uvcSxyfreCvoxqkbvKd2mnu9gZLbNdgT/PGeKwSSFpJSUGtNll2CXEoJUi7yQ05GE0WTF2m6hr5MLvviLhoCoepuK2V5J9J5334sdgtT50cRmkAaEl9XHR3v2Ed4OsiV506XpSgDRdvJtGT85DU637l/3eQGeLRumhf91aSM6+4TGgYVmsE9vsJWygU0AfdX+7m/unS7QvzTZN2iaoGpjIW5PH9oyQFXiAdr/Dw11UC8gOuHBUkKDZtIY9Ukn8m4VwgBN1cFuXmdK++lkylG3xjAPzhFMmw+DIUuQELzbV003tRecLuL3z2BTBe76OCoqsDmshefjTAkfc+dwuF10nikHW97LXNDM4ssEkLXaL2jZ20nqFgrXwT+UQjxSWAQ03qMEOIY8Gkp5e9kvncCbcBPlmz/P4UQdZg/9LeBT2+O2LsLKSWDP72wOLBVmrN6gy9d4MhH71nX4KkaW5Jfaxpf8XbHPHN8c6KBybjIGjIEErsmebS2vAC/cFrj5wFfnoU6IXWemanl2elaNM1Mk5mUGu32KI/UzHCfL4BDN0hL+N8TDTwzU0M4rdNsizOesBVwDxFMJ2yMxW1FC7IU41TQxV9c7cTADC78ud+TFxcTN3T6opW8HPByn6+0wruA15oyM36w8vLdy+VjziUVTzL08uWsO2ZFjcuccZ6PMvjTiyVTfS5kJipGzd5GRl7vz7MuC03g7Sy97Y2AUpZ3IEITtN21h+Zbu0iE41grbVjsVqYujDD0Sl/BqZTVEJ4q7O6xFupsSb7Qc4W/Hm6hP2oGfBx2hfid1hEcG+SHljIEx6cKVQss7yElMKc4K3WDL/Rc4T8OtjOesKMBlXqajzWNMhG3M5Kw01MR4Z2+wLqkXCpGPBTl7D++lqfQLqQYHDsxgMNTibejdtH6yHSwZMAewMgb/QSGZqg72ML0+ZG87BXSkERnwkRnwswNTtP+jn04PBVMnR8hnUjj8Dlpu7OXqpbqdThTxWqRUs4ADxdY/gbwOznfrwItBdo9tJHy3Sgkw3GS0cIBvOlkivh8dFu4JVkE/GHPFf7XWCMvz3lJS8ERV4jfaB4tOwB5OmHLKML565ILgdQ5w8lEwka9PZH1af4v19p4Y96TDf4eTTgoZs3RhCS2JHjuWtTB96drGYk56K6M8L7aaRpylOmUIfjLwY5FweVmQawCJcENnZ/6fWUry5uBNCQXv3vC9CvOjP3RmRCXvv+W+QJWxG1OaILuhw8vWyW17kAL/v5JorNhM4ZJmBbphpvat8VvdKtRyvIORrdZqMhMuafiyXVVlMFMIzN7ZQJvZx2avn7OtZ0VMf54zxXihkCDdXVJKIQ/ZVlLbQ00JDbN7Nc2R5w/23eZ6YSVpBQ02BLL+gOuN33PnC5q+QXT93j85GCesjw/Uro0+cK24Yk5qnsaaLy5k4lT10gnM8GfMr/t0MuXOfrr99Jyew9SSpViSKHIQehaUWuflKzruLpWXJY0n2ob4VM5GYNWQq0tsSK3trjU+f5UHUfdIaYSVl6f9xTwmy68P11I2nKyRpyYd/OXgx2kpFkE6mq0gpf8Pj7fNcAep2kguBB2FlG9i+Xo2F7Mj8ySCMfzjSTFZgoBm8vBgSeOlXTNW0DTNfY9dguBwRkCg1PoVgs1exs3JHZpJ7J97lTFmgiO+NfdrygRjDH404uc/dZrJCJx009qHbFrcsMVZQC3JVUw0rtchMi/UWptSZrsm68oG6k0Mf/ykemJUH6QiGa1lPUEMFJmCeqmmzs4+hvv4MiTdxd318n4S0MpnzeF4sbEWmGjokggn+nytHsywjh1g3f4/NjE4lk1rUSu+QthJzFDcDVagaVotgyZU9pJYhMGv9U8ki3gZEiy6UgXqqWmM1mMvjLcmt3LSmoE2kWady7jFrjZRKaDGMmVzVgKTZSlKF9vr+HrqqPrgYO037tXKco5KMvybqEMPcVaaQZvrQQjlSYRTHP6f70MmAFg7ff04uvaOQUHHJrkHV4/P1viT6djYCCwZcrCpgokMBJIOh3RDXWrWAmlLMq5VNa6zfZSEhz1M3N53DT6VZASAAAgAElEQVRwle3LbjYUwhxsF3zj89tRVrJ7heJGpeuBg1z87gmMVBojZaBZNISu0fXQoa0Wbd357ZYRdCQ/8Vdns2Hc5A5yct6d8Q1eTFoKnpmu5ZArVHRocmppjrhDDEYraLTHebx+kn3O6+5kQzFHkfScMJ6wMZ/SqbKk2e8MYxRJ46mx4CEisGtp9lVGuGuZAL3NxuZyoFn0TJrX5RGawNNeu3xDRVkoZXmXUNVSXVSR0qwajTd1YPdWMvDCuYLZC8olFU3Q/+NztByL0Xi0Y/kNtgm/1TJKzNB4Y96DVUiSUnDUHeTjzSOcDFYRMzS8liR/PdJKWgqSMjdYb3irxc+i2yzLv/QIsLnsJCMJ+p47RWRqZcGFmkWjpvd6+l7dquNq9BIc8+cp27rdQkW1KletUBTD4ank8Efuxt8/QXQ2jMPnpLq7fkUWv52CRcAnW0f5WNM4/pSFamsSi5D87vkDzKXyjREGgp/7vby/bgqfJcl4YnHeYZtI81jdFE80TJU4pizqZne9SqtpNPlEywh/N9JCMlNq2iIMrBh8vGWUsyE3KQn3eOe4rapwKrutxNtZx9Arl/OWC13g8DiJzUWuu2FqAt1uoeHI5hYd283svrv1BkW3Wei4bx+DP72INMwURZpFw+Kwsv/xY1grbEjDYMhuIVUk4KRsJIy8OUDdgZYdM+BbNcn/0TFEIDnGeMJGvS1BdSYK+ZGccqSHXWFe9PsYiTno2oRgvdXQ9cBBLj39dvEGEqYvjjN1YWxVL0YV1S6q9yyuddFx334uHH8TI5nGSKURuobQBD0PH1buFwrFMuhWndp9zVstxqbh0A2a9Osv9A9Xz/C/JwvPRgph/n2ue4AvDnQxk7SiYZbbvts7x/vriyvKAM32OB5rislEfjW83ooIzpxiVA9W+2lzxPjBVC1TSRsHnGHeWzuNz5rigertZUleim7V2fu+m+l79jTpeAqEOdPYcqybuoMtTJ4dZvrCKEbawNtRS9PNHVgrys+RrSiNWG8/1PXicF2d/PYTv7rVYuw4YoEwUxdGSYbjuJt9VPc2oluvDyKxuQh9z5wiHoyWPyVfhMp6t5k+rKMu6y+diiVIxVPY3Q41Pb9GZI4rxNLlY28NMP72NXM2QbDma5nL0Y/fh6XAS5AZ8DlJeHoeR1UlNXsaVanqEuz/6l+9KaU8ttVybCZq3FYUYjRm57OX9+QF8NmEwUcax3i0bgYwvb/6oxX4k1a6KyNZg8Zy9Ecq+Pf93aSlmarOLtLYNMkf9/YtyoixG5BSEpkOkk6mcda50a07w2C1Eyg1Zqte3mU4vE7a7tpTfL2nkkMfupPoTIi54RnmhmZIhhM469z4B0q/wS8lMhlk4MVzuBq9dL7zAIMvnSc4FkBoptWx5Y5u6vabWamMVJr4fBSLw4q10r6mc9ztpGJJhl69jH9gCmkYuBu9tN29J1voY/zkIBOnhq673ayjomz3VBZUlAE0i07tviZq9zWt3wEVCsWup9kR5/11k3x3qj7rAmHX0rQ7Yotm9oSAnsookF+gqhTdlVH+cv8FXvL7GI3b6XREeYcvQIW+ftmhtgtCCBV4twUoZfkGRAhBZa2bylo3TTd3IqVk5I3+Ve1LpiXhiTkuHH+TZDQBhkQaptvC8Kt9WB02YvNRxk5czU4bueqr6HroEBaHlfDEHLG5KA5PBc4Gzw0/pS8NgwvH3yQeimVdKIJjAS589wQHf+V2rBU2xt8ezMt/vFIcvkoSwdii/Qhdo/3u4i9aCoVCsVo+2DjJLVVBXpz1ETV0bvfMcaxqvuyy0svhtqR5X115BVQUipWilGUF0xdGmTq7+iA2I2VgpOIFl197+RLpRGqRUhYcn+PS028BkAjGIZNayF7loOvBg4Sngggh8LTV3HDT/IHBaTN4b4mvsZEymDg1RO3+ZsquiVoMAb3vOcr8yCwTJ6+RjCSoqHbScns37ibf2vatUCgUReipjGYsxwrFzkIpywrGT14raqkUuoaryUtweJmCFkX8ZgtmbZDyeiW5nG2is2HOfft1NItZflRKSfu9e6ndu/2m/aWUpOMpNIuGZllaGXD1hCbmC6cGkpLQeICmWzpKp48TomTJU4C977sFu8tB3b5m6m6goCOFQqFQKFaDUpYVJCP5VmEANEHrnT1UVru4PB4oqVAj5cqKlpRomnucaz+/hKvBs63Kbc4Nz3Dt55dIhs1+87TV0HHfPiyO/MhjKWUmF3F51mC7y4HQtYKVGK0uO9ZKO5U1TsKTi9PBCU3gbvFhr6pk+sJo0UqOzce6cTd6y5JFoVBsPsGxAFMXRkjHkng6aqnd27SuL+SK7Yk5A5vGUmG74d0RtyNKWVbg8FQSLVAVTgiBu9GLw+fEWV9FcDQ/tY7QBa5GL8lIjNhcdJH7gKZr6A5rVqlcDdKQTF8cpfWO3lXvYz0JTc5x5bkzi5TRwNAMse+9xcFfvSM7yBmpNCOv9zN9cRQjZeDwVtJ29x6qWqpL7r+6t56RN/rz3iU0i0bjkXYmzw0Tmcm/VpV1VXQ/eIioP8zMpTFkoWx3QtB4tH2lp6xQKDaJkTf6mTwzlDUYhCbmmDw7zIHHyytZrNh5JKMJrr50nuCIH4TA4rDSfs9evB2qoMh2QuX2UtBye7dpHc5B6BquBg8V1S6is2FCE3MFt7U4rAjN3EdVkw+hCTSLhm6z0HxHYSum0ERZFQcBkHLFVQc3krETV/OttoYkEY4zP3LdVeXKc6eZujCafejFAhH6nj1NcLx0Lk+Lw0bve25Ct1vQrDqaVUfoGi23d2NzVzD82pW84wtdUN1jFjkoWQ41Y+VWKBTbj/h8lInTQ4tm1oyUQSIYY/zk4BZKptgopJRc/N4J5of9SEMi0wbJcJz+H58ltMyzQrG5qFdVBZ72WjrvP8Dwa32kogkQgurehmxmhPG3ryLThbWsZDhBMjxLaCxA29176HrwIKl4EmuFjQvHTxCfj+Rt42mvwdXoYfSNgWWzOmgWHU9rzdpPcpVIKZkbnGby7DCpeJL4fOHgFCNtEBzxY3M6MNIGofG5PKVWpg1GXu9n/y/fWvKY7iYvRz92L6HxOYy0gavBg26zMH7qWkF/ZJmWTF8Yo/5gK876qqI+zc46d9nuIAqFYnMJXCucyUEaktn+SVpu71mX46QTKaYujDI3OI1ut1J3sJmqlmo19b8FBEf8pjFoybgu0wajJ66y99Gbt0gyxVKUsqwAoLq7Hl9XHelECs2io+VYmiMzoWW3N1IGQ6/0Ud3TgM1i59IP3iYWKODaoWv4uuqp7mnA7qpg9M0B4sEYNqedZCxBOpHODhxCE9hcdrxddet3oitk+LU+syrScqnaDMnk2WGmzo+YPsdFTLjRMvoSQGga7ubFmSlk2ijqF25kFHPdZqH5WBdjb+a8iAjTJabtnr1lHVuhUGw+pZTV9VJjU/Ek5596g2Q0kX2ZD47OUneghdY7t4er241E1B8qGl9SyDVSsXUoZVmRRQiBxZ6fqs3uqShqUV28AwhPBZm6MEJ4Yr5gE5k28A9MIXSN6QujpBIp7J4K6g+1UtVSzdhbA/gHphEaVPc00nxrJ5qukYolmbk8RmQ2TGW1E1eTj9m+CaIzQSpqXNQfasXurih4zKg/zOTZIaL+MBabeX6WChu1+5pwNXjy2hupNIGr04Sn5pk8P1J2yWhpSNOqW0KxtlQUToVnpA3Ck3Nmwvn6qoLVDz1tNYyfzM+xLDSBr/v6C0XjkXYqvE7GT14jEY7hrKui6ZZOKnzOss5DoVBsPt6OWoZ/cSVvudBFXvn51TJ+8hrJSHzR7JORMpg8N0Lt/uZtFUh9I2CvqjSNK0a++5zd7dgCiRTFUMqyYlmajnYQGg1krZelMBXN0pUA54dmFrVJhuNc+/lFfF31dD1wkI53LG4fnQ1x8XtvYaQNU9nWxKIyz8GJOaYvjLLnvUdx5fhISykJDE4z8OI5ZAEF1t8/Qf2h1kXTm7G5CBe/ewIjbZT2/wU0q24qrmVmAREWjcabrgfYzQ3PMHl2mJg/QjISz/qNC03Q/eAhnA1VTJ0bYfbKJEIT1O5rwttRS2BwOqswC13DWmGj4XDbomN52mrwtG2d+4pCoVgZNpeD5tu6GH1zAGkYIM3AXntVBQ1H1icw198/WdhNS0rmrs3gOFK5ZLEZM6Lp2g2X834z8LRVo1stZrrQnMui6RpNt3RumVyKfJSyrFgWV6OX9vv2MfTyZaSURZVIfSEgTdOQ6eKKZiGlW6Yl/oEpGo+G8yyg/S+cI51IXW+7tMyzITEMycBPznP4w3dhJNMMvXqZ2SuTRae4IFPo48ww1b2N2WP2/+gsqViy6DYLWJ02eh4+wrWXLxGZDhZsI3SBEFqmcqFB/YEWs6gI+VHv5nld77O+505jc9pIhK9Plw4Fwjjrq2i/bz/T50cxkim8XXXUH2xVkfIKxS6g8aZ2qpp9TF8cJRVP4WmvwddVv8gtbi0UjVkQIm/d/PAsgz+7aFZmlRJng4eu+w9gc+18i6eRNpgfniUZTeCsq6KyxrUlcghNY98v30L/82eIBiLZa9B6Z48ydmwz1vSEFUJ8CPh3wAHgDinlG0XavRf4S0AHviql/OJajqvYfGp6G6nuric6G8ZIGwy8cI5ULImRSptFRISg55Ej2Fz2kgqqWLAKF0JK5kdmqfA5ScUShKfmSUSSBYMEC5GMJJg4PcTI61fKzvog0wb+q1NU+JzEgzFic8sfS+gatfuacdZXYRjFz1W3Wel+6CCR6RCuRg/OuioAEqEYE6eHSvaTNAziwdii85Apg8hkEMsRC/seu6W8E1QoFDuKylo37bX7NmTfNXsbGXtrsODY4+287soVmQ7S99zpRe1C43NcOP4mhz9y97op71tBZCbE5affxjAMpCERgKvJS8+7jqz4vEKTc8z2TYKU+LrrcDV6VxwoaXdXcOADtxMPRkknUji8zh3dv7uVtZqjzgC/AvxVsQZCCB34z8C7gGHgdSHEcSnluTUeW7HJCE2jstYNwKEP3Ung6hSR6SA2dwXVPfVZf2dPRy1z12YWDbRCE1TWuQlPFvZlXmijWTQGf36R6QujK05zJqVk5I3+FW/nvzLBxKlrCzsp2Vaz6FRUO7PuFIlQ8RzSqWiCS0+/jWbRkYakssZF98OHmR+ZNQvtlTyZwouNVJrA4LSyOigUihXTcLiNwOA0MX/YnNUS5rjeekcPNqc9227s7QIpMqUknTTd7Kp7GjZZ8vVBGpK+H54kFb8+eygxC8GMnRgoO+OIlJKhVy4zc2ksOzs4c3kMT0ctXQ8cXFVmkWIxN4rtwZqUZSnleSgdxQvcAfRJKfszbb8BPA4oZXkHo+ka1T0NBQfNrvsPMPizi/gHJk2XDENSd7AF/8BkSQ1RSkgEY0xfGFtVPmBNE8tnrShAtvT2cgiBp72GzvsPZN/8dV2j5BElWbeV8OQ85596HWBVcpoymO4uCoVCsRzSMNNYGkYmBaXVwv5fvpXA4AxzQzNY7BZq9jblub5FZwtnYjCS6fLHy21IaDxAOpXvIijTBlMXRrPKciqWZOLsEIGr0+hWnboDLVT3NmR1ndD43CJFGcwxfW5wmrlrM6qgyC5kMxwdW4ChnO/DwJ2FGgohPgV8CqDZtTU+RIq1o1l0uh44SNvde0hFE1idDoIjs0yfHy2xkaDrwYMMvnRh+YC5TGAfmgBDmr7BmhnoVlbWjtUiJf7+SXS7lY57zTRstQebGXvzatm7KMcfGkx/ZyAvv7XQNKp71ycyXqFQ7F7mR/30P3/merpJKWm9aw91+5vxddXhK5GS0+GpLDiWahYde9XaLaCxuUg2yNvbUYvDuzmZenItyktZMGqkYknOPfU6qWgi6zIYnQ0xNzRD90OHAJjpGy9o8DBSBtOXxpSyvAtZVlkWQjwPFHo6f15K+Z31FEZK+RXgKwCH6+pUrbEdjsVuzbpmhCbnzYjfAug2nUMfuguL3Up/TiBfHgIcXieuRg81vY0Ex/xEZ0NU+FzU7m9m9MQA8WB0w6vUTV8YpfX2bnSbheruBqbODpOKlZB7pQjoeeQI/v5J/AOTmelSMwCn6eaOLQtGUSgUO4NkJMGVZ0/lKXTDr16mwucsmDIzl8abO5gf9ecXVpIST1v1mmQbeaPfjNnIVBQdPXGV+sOttJbhAjE3NMPEmSGSkQRVzT4abmpf5D5SimQkTiIUK5rVacHFcOL0tUWKMmSsxtfMdKLOuqrS8SZlZI1S7DyWVZallI+s8RgjQG5eq9bMMsUNhN3lQLNoBd/GnXVVWCtsAFhddpIl/ID3/tJRrJXm4Lh0wG880s7s5YmCSrmnoxaQzA3NoummawhSFg82LIWUmeA7mUlpVzrF3EoQuoantTqb+q3uYAuBwWmEJqjubsDhVXlQFQpFaWYujxUsYGRmABpaVll2NXhoOdbN8Gt9i5ZLKbny/Bn2vu+WVfnlhibmmDyzOLhZpiVTZ4fxtFbjbvIV3Xb0xAATp65lnyGxuQgzl8fZ//hty+aHHnvrKmNvXzWzExXoF82i0XaXWZTFf3W64HNhIYOGs64KX3c9gatTec8zzaLtWH9uRWk2I+TydWCPEKJLCGEDngSOb8JxFdsIX089FBhcNYtGQ07u4ZZj3UXTG/k667OKspFKk4onFz0Q7FUV7HvsFpx1btNVQ0BlnZv6Q60ER2YJjgYQQmCttNPzyOE1nY/NaefqS+fz8mOuBk3X0Kw6Vqed5ls76X74UHads66KlmPdNN/apRRlhUJRFvFQLM+Fa4FEMFbWPsLTBYKxDUlkOkh4Ym5Vck1fHCvuvnBxrOh2yWiC8ZPXFm9rSNKJlJn9qATzo37GTw4i0zI/n7FVp6qlmr2P3Yqz3nyB0CyF1SIhRHadp60GV6N3UVuhCxw+J77u+pLyKHYma00d9wHgPwJ1wPeFEG9LKd8jhGjGTBH3qJQyJYT4l8AzmKnj/kZKeXbNkit2FBa7lT3vPcqV505np8GkIWk51k1Vy/VpvZreRjPS+OXLi/I526oc2D0VhKfnGX9rkLmhGXO5y0H7vXuz+6isdbP/8WMYqTRCEwQGp7n6k/OLBtn4fISrL50vWjlpOXS7edusKNBFZP5ZYtUQukbzsW4ajrQV3EyhUChWiqvew+zlAn61mlhUuKkUobHCCrFhSEKT82XvJ5d0CTe7UuuCo34z7WiB4Xp+eLbkMSfPDhcOqNYE9Ydbabmte9Hiuv3NDL/Wl7+NEPi66jMfBb3vPsJs/yQTp4eIBcJIA6IzIS49/TZdDxxQ2S12GWvNhvEU8FSB5aPAoznfnwaeXsuxFDsfV4OHmz56L6HJOYxk2ozOLlBMo3ZPEzW9jcRDMS7/4CSpSJzEfIzxk4OMvz14PcAPiM9H6Xv2dMaiXIWUMmMBMDNGjL2VXx4aCelkuuwy1osQ0P3w4YzyuzwVNU7a791HZbWL8OQ8fc+ezhQpMY/taauh/lDryuVQKBSKIvi66hh9c4BEOL7oBV3TNRqOlDfeWCqsJCP5LnFaJpi6HGJzEeYGp0GYeZy9nbXMD88UdF/wdRYPOCxm7QWylU+LkYokCq8wZMF1tfuamBuaITjqx0gZ5kynELTd3buoIIvQNCqrXcTnIlkrvpQQnpzjwvETHPnIXdnnkGLno8p+KTYVoQncZVgkhBBMnRshGYrlV+xbouPKtMHVn5wnFUuSiiWxVtppvq2T2n3NJEJFphwluFuqCY35Fw3cQhPY3A40XaeqrRqL3cLUuRFSsSQVNW7a7urNFhepqHETmcqfqrQ57Rx+8u48nz53s4+bPnYvc4PTpBJJ3I1eKqpVsJ5CoVhfNIvO/sdvY+iVywSuTiGlxN3ko+3uPdic5VXgazzSzuDPLhSwsIK3c/lsDyOvX2HizHBWWR95Y4DGo+04vE6i/nDWb1noZklvXwlf36qW6oLubkIT1CyTHaiqrZrIbDDPLUWz6ItmNa/vU6PnXUcIT8yZMS5Wneqe+oKW4vGTg/kBg5l0of6BKWr2qMxFuwWlLCu2LbOXx8sOwMt1iUhG4gy9chkjZeDwOQv61wkBjTe1Ee+sZeztQZKRBI6qCppv78bbvvhB0HhTR8Fjdt2/nwvHT2CkDWTaQOgaQgi6HjpUNPhFt+pU96oAEMXGstbqqkKILuAbQA3wJvAbUsoiJjrFdsRaYaP7oUPZuI6VBuT5euoJTweZOj+SjSNZcD/QraVVh+CYn8mzw0syQ0gmTl2j971HCU/MM9M3DlJSs6eRuoOtJavWaRad7ocPceX5M4BEpiWaVcfudtB8W1dJWeoPtpoGDyOZVbgXjCLFlH4hTHeV5VxNIjOhgkq8kUoTnQ2V3Faxs1DKsmLbsqpMFRmMlMHomwN0P3Qor2yr0AR2TyWuRi/uJh+1+5pXdQyH18nhD9/F9KUxItNBHN5K6vY3Z4MQFYotZK3VVf8U+JKU8htCiC8DnwT+68aLrVhvVpO1YmG7trt6aTjSRmg8gG6z4G72lVWKeerCaOFAvrTBbN84He/YT+PR9gJbFsfTVsORj9zNzJVxUpEErkYvnraaogHhC1gcVg584HZG3+i/nlmot5HmWzsR2tpyHDi8zoKxK5pFUwHZuwylLCu2Ld6O2oz1YXXby7SBw1dJ14MHGX7lMsmoaRjzdNTS8Y59q36I5GJxWLOlrxWK7cJaqqsKIc4DDwEfzbT7GqaVWinLNyA2p33F6dDS8SLBerLEujKwVtpoPLLy8dbmtNN5/4FVH7cYjUfbC/pgC13D161mEHcTSllWbFuaj3UzNzRDOpnK+psJXaBbLWYKICGQhszmTF6KBHSbFV9nHd6OWtLxJJpFV0EXCoVJseqqNUBASpnKWd5SbCeq8qpiKd7OWoLjAWSBQD5viUC+nYazroquBw4y+LOLGCkDKSX2KgfdDx1Gt6rnzG5CKcuKbYvNaefQB+9g8uwIc8MzWCts1B9qxd3sIxaIYCRTVFS7uPL8GYKj/kVuG+abfX12wBJCYHGUF8GtUOwENrO6ailU5VXFUmp6G5k8M0w8GFscyOep3FXKMphZPjzttcTmImiZYEXF7kMpy4ptjcVho/m2rrwgjgqfM/u568GDXHnuDOGpeTMXpyFxN/vouHfvZourUGwaG1hddQbwCiEsGeuyqrqqWBGaRWf/+29j4swQs30TIKBmTyMNh9vK8nneaQhNLHomKXYfSllW7Hgsdiv7HruFWCBMfD6Kw+tUb/cKxfJkq6tiKsNPAh+VUkohxAvABzEzYnwC2DRLtWJ3oNssNN/aRfOtpbNVKBQ7gd33iqe4YXF4nXjaa5WirLjhEUJ8QAgxDNyNWV31mczyZiHE0wAZq/FCddXzwD/mVFf9feD3hBB9mD7Mf73Z56BQKBTbBWVZVigUil3GWqurZjJk3LGRMioUCsVOQVmWFQqFQqFQKBSKIihlWaFQKBQKhUKhKIJSlhUKhUKhUCgUiiIoZVmhUCgUCoVCoSiCUpYVCoVCoVAoFIoiKGVZoVAoFAqFQqEoglKWFQqFQqFQKBSKIihlWaFQKBQKhUKhKIKQUm61DAURQkwBgxuw61pgegP2u9NQ/WCi+uE6qi9M1qsfOqSUdeuwnx3DGsbt7fLbU3Lks11kUXLks11k2S1yFB2zt62yvFEIId6QUh7bajm2GtUPJqofrqP6wkT1w+azXfpcyZHPdpFFyZHPdpHlRpBDuWEoFAqFQqFQKBRFUMqyQqFQKBQKhUJRhBtRWf7KVguwTVD9YKL64TqqL0xUP2w+26XPlRz5bBdZlBz5bBdZdr0cN5zPskKhUCgUCoVCUS43omVZoVAoFAqFQqEoi12vLAshPiSEOCuEMIQQRaMkhRDvFUJcFEL0CSE+u5kybgZCiGohxHNCiMuZ/31F2qWFEG9n/o5vtpwbxXLXVwhhF0L8Q2b9a0KIzs2XcuMpox9+UwgxlfMb+J2tkHOjEUL8jRBiUghxpsh6IYT4/zL9dEoIcetmy7jbWOtYLIToytybfZl71bZKOZYdC4UQD+bcA28LIWJCiCcy6/5OCDGQs+7mjZIj067gmLzJ/XGzEOKVzPU7JYT4SM66NffHWsZnIcTnMssvCiHes9Jjr1CO3xNCnMv0wY+EEB0569bt2bmWcVoI8YnMtbwshPjEBsvxpRwZLgkhAjnr1rM/Vj1er1t/SCl39R9wANgHvAgcK9JGB64A3YANOAkc3GrZ17kf/gz4bObzZ4E/LdIutNWybsC5L3t9gX8BfDnz+UngH7Za7i3qh98E/tNWy7oJffFO4FbgTJH1jwI/AARwF/DaVsu80//WOhYD/wg8mfn8ZeAzq5SjrLEwp301MAtUZr7/HfDBdeiPNY3Jm9kfwF5gT+ZzMzAGeNejP9YyPgMHM+3tQFdmP/oGyvFgzu/gM7nPiWLXaYPkKDhOZ36r/Zn/fZnPvo2SY0n7fwX8zXr3R2Zfqxqv17M/dr1lWUp5Xkp5cZlmdwB9Usp+KWUC+Abw+MZLt6k8Dnwt8/lrwBNbKMtmU871ze2fbwEPCyHEJsq4GdwIv/OykFK+hKkAFeNx4L9Lk1cBrxCiaXOk252sZSzO3IsPYd6bsLYxbKVj4QeBH0gpI6s83nrJkWWz+0NKeUlKeTnzeRSYBNar4M5axufHgW9IKeNSygGgL7O/DZFDSvlCzu/gVaB1lcdakxwleA/wnJRyVkrpB54D3rtJcvwa8PerPFZJ1jBer1t/7HpluUxagKGc78OZZbuJBinlWObzONBQpJ1DCPGGEOLVhWnHXUA51zfbRkqZAuaAmk2RbvMo93f+q5mprG8JIdo2R7Rtx40wJmxHivV7DRDI3Ju5y1dDuWPhAk+SrwT8SeYe+ZIQwr7BchQak7esP4QQd2BaGq/kLF5Lf6xlfF7P+3Sl+/okpjVzgfV6dvpdbWkAAAPJSURBVK5lnN6S/si4o3QBP85ZvJm6RDFZ160/LKsWbRshhHgeaCyw6vNSyu9stjxbRal+yP0ipZRCiGJpUDqklCNCiG7gx0KI01LKK0XaKnYf3wX+XkoZF0L8c0xrzkNbLJNih7BdxuJ1GgvJWKeOAM/kLP4cplJpw0xV9fvAH22gHHljMqayWDbr3B//A/iElNLILC67P3YLQohfB44B9+cs3sxn53Ybp58EviWlTOcs21W6xK5QlqWUj6xxFyNArgWtNbNsR1GqH4QQE0KIJinlWGbAmyyyj5HM//1CiBeBW1hsQdiJlHN9F9oMCyEsgAeY2RzxNo1l+0FKmXvOX8X0Z7wR2RVjwmazgWPxDObUqiVjWSx5PdZjLMzwYeApKWUyZ98LVti4EOJvgX+zkXIUGZO/zSb3hxCiCvg+5ovPqzn7Lrs/irCW8Xk979Oy9iWEeATzJeN+KWV8Yfk6PjvXMk6PAA8s2fbFVchQlhw5PAn87hIZN1OXKCbruvWHcsMweR3YI8zoYhvmhd81mSAyHAcWIkE/AeRZeYQQvoUpNCFELXAvcG7TJNw4yrm+uf3zQeDHMhMhsItYth+W+OW+Hzi/ifJtJ44DH89EWd8FzOUoBYqNo+BvNHMvvoB5b0KRMaxMlh0Lc8jzw1y4RzI+s08ABSP010OOYmPyZvdH5lo8hekX+q0l69baH2sZn48DTwozW0YXsAf4xQqPX7YcQohbgL8C3i+lnMxZvp7PzrWM088A787I4wPezeJZkXWVIyPLfszguVdylm22LlFsvF6//ignCnAn/wEfwPRTiQMTwDOZ5c3A0zntHgUuYb75fH6r5d6AfqgBfgRcBp4HqjPLjwFfzXy+BziNGfV6GvjkVsu9juefd30xpwrfn/nsAL6JGSDyC6B7q2Xeon74f4Czmd/AC8D+rZZ5g/rh7zEj+pOZ8eGTwKeBT2fWC+A/Z/rpNEWyN6i/FfX5msZizKj8X2Tu0W8C9lXKsexYmPneiWmZ0pZs/+PMb+IM8HXAtVFylBqTN7M/gF/P3Ctv5/zdvF79sZbxGdPKewW4CPzSGn+jy8nxfOa3u9AHx5e7ThskR9FxGvjtTD/1Ab+1kXJkvv874ItLtlvv/lj1eL1e/aEq+CkUCoVCoVAoFEVQbhgKhUKhUCgUCkURlLKsUCgUCoVCoVAUQSnLCoVCoVAoFApFEZSyrFAoFAqFQqFQFEEpywqFQqFQKBQKRRGUsqxQKBQKhUKhUBRBKcsKhUKhUCgUCkURlLKsUCgUCoVCoVAU4f8HpgOgQTXSTWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_0, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_0, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aeHx9MbTvrJH"
   },
   "source": [
    "Oh wow, it seems like we've found the cause of model's performance issue.\n",
    "\n",
    "It's currently trying to split the red and blue dots using a straight line...\n",
    "\n",
    "That explains the 50% accuracy. Since our data is circular, drawing a straight line can at best cut it down the middle.\n",
    "\n",
    "In machine learning terms, our model is **underfitting**, meaning it's not learning predictive patterns from the data.\n",
    "\n",
    "How could we improve this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6VivsLTmHC8B"
   },
   "source": [
    "## 5. Improving a model (from a model perspective) \n",
    "\n",
    "Let's try to fix our model's underfitting problem.\n",
    "\n",
    "Focusing specifically on the model (not the data), there are a few ways we could do this.\n",
    "\n",
    "| Model improvement technique* | What does it do? |\n",
    "| ----- | ----- |\n",
    "| **Add more layers** | Each layer *potentially* increases the learning capabilities of the model with each layer being able to learn some kind of new pattern in the data, more layers is often referred to as making your neural network *deeper*. |\n",
    "| **Add more hidden units** | Similar to the above, more hidden units per layer means a *potential* increase in learning capabilities of the model, more hidden units is often referred to as making your neural network *wider*. |\n",
    "| **Fitting for longer (more epochs)** | Your model might learn more if it had more opportunities to look at the data. |\n",
    "| **Changing the activation functions** | Some data just can't be fit with only straight lines (like what we've seen), using non-linear activation functions can help with this (hint, hint). |\n",
    "| **Change the learning rate** | Less model specific, but still related, the learning rate of the optimizer decides how much a model should change its parameters each step, too much and the model overcorrects, too little and it doesn't learn enough. |\n",
    "| **Change the loss function** | Again, less model specific but still important, different problems require different loss functions. For example, a binary cross entropy loss function won't work with a multi-class classification problem. |\n",
    "| **Use transfer learning** | Take a pretrained model from a problem domain similar to yours and adjust it to your own problem. We cover transfer learning in [notebook 06](https://www.learnpytorch.io/06_pytorch_transfer_learning/). |\n",
    "\n",
    "> **Note:** *because you can adjust all of these by hand, they're referred to as **hyperparameters**. \n",
    ">\n",
    "> And this is also where machine learning's half art half science comes in, there's no real way to know here what the best combination of values is for your project, best to follow the data scientist's motto of \"experiment, experiment, experiment\".\n",
    "\n",
    "Let's see what happens if we add an extra layer to our model, fit for longer (`epochs=1000` instead of `epochs=100`) and increase the number of hidden units from `5` to `10`.\n",
    "\n",
    "We'll follow the same steps we did above but with a few changed hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j-GRUN9QHC8B",
    "outputId": "9174e74b-862a-4bca-9f17-c7176d22f81e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CircleModelV1(\n",
       "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
       "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class CircleModelV1(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10) # extra layer\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        \n",
    "    def forward(self, x): # note: always make sure forward is spelt correctly!\n",
    "        # Creating a model like this is the same as below, though below\n",
    "        # generally benefits from speedups where possible.\n",
    "        # z = self.layer_1(x)\n",
    "        # z = self.layer_2(z)\n",
    "        # z = self.layer_3(z)\n",
    "        # return z\n",
    "        return self.layer_3(self.layer_2(self.layer_1(x)))\n",
    "\n",
    "model_1 = CircleModelV1().to(device)\n",
    "model_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ACkcim2k2G5R"
   },
   "source": [
    "Now we've got a model, we'll recreate a loss function and optimizer instance, using the same settings as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "AXYwYPpSHC8B"
   },
   "outputs": [],
   "source": [
    "# loss_fn = nn.BCELoss() # Requires sigmoid on input\n",
    "loss_fn = nn.BCEWithLogitsLoss() # Does not require sigmoid on input\n",
    "optimizer = torch.optim.SGD(model_1.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drHt2W7x1JEW"
   },
   "source": [
    "Beautiful, model, optimizer and loss function ready, let's make a training loop.\n",
    "\n",
    "This time we'll train for longer (`epochs=1000` vs `epochs=100`) and see if it improves our model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aX0QGBozHC8C",
    "outputId": "b00e48e9-1075-4f6e-c0c3-e511451d3fe9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69396, Accuracy: 50.88% | Test loss: 0.69261, Test acc: 51.00%\n",
      "Epoch: 100 | Loss: 0.69305, Accuracy: 50.38% | Test loss: 0.69379, Test acc: 48.00%\n",
      "Epoch: 200 | Loss: 0.69299, Accuracy: 51.12% | Test loss: 0.69437, Test acc: 46.00%\n",
      "Epoch: 300 | Loss: 0.69298, Accuracy: 51.62% | Test loss: 0.69458, Test acc: 45.00%\n",
      "Epoch: 400 | Loss: 0.69298, Accuracy: 51.12% | Test loss: 0.69465, Test acc: 46.00%\n",
      "Epoch: 500 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69467, Test acc: 46.00%\n",
      "Epoch: 600 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 700 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 800 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n",
      "Epoch: 900 | Loss: 0.69298, Accuracy: 51.00% | Test loss: 0.69468, Test acc: 46.00%\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "epochs = 1000 # Train for longer\n",
    "\n",
    "# Put data to target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_1(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> predicition probabilities -> prediction labels\n",
    "\n",
    "    # 2. Calculate loss/accuracy\n",
    "    loss = loss_fn(y_logits, y_train)\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_1.eval()\n",
    "    with torch.inference_mode():\n",
    "        # 1. Forward pass\n",
    "        test_logits = model_1(X_test).squeeze() \n",
    "        test_pred = torch.round(torch.sigmoid(test_logits))\n",
    "        # 2. Caculate loss/accuracy\n",
    "        test_loss = loss_fn(test_logits,\n",
    "                            y_test)\n",
    "        test_acc = accuracy_fn(y_true=y_test,\n",
    "                               y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening every 10 epochs\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o0ca3sIV1WrZ"
   },
   "source": [
    "What? Our model trained for longer and with an extra layer but it still looks like it didn't learn any patterns better than random guessing.\n",
    "\n",
    "Let's visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "GUjJqRw7HC8C",
    "outputId": "12d163f9-b602-459e-f34b-54c58abf8d7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_1, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8UqtuZYwHC8C"
   },
   "source": [
    "Hmmm.\n",
    "\n",
    "Our model is still drawing a straight line between the red and blue dots.\n",
    "\n",
    "If our model is drawing a straight line, could it model linear data? Like we did in [notebook 01](https://www.learnpytorch.io/01_pytorch_workflow/)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nam5esXj2Mj_"
   },
   "source": [
    "### 5.1 Preparing data to see if our model can model a straight line\n",
    "Let's create some linear data to see if our model's able to model it and we're not just using a model that can't learn anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xLoEBQ6fHC8C",
    "outputId": "176a4674-3142-420c-bace-97cdbfbf473e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[0.0000],\n",
       "         [0.0100],\n",
       "         [0.0200],\n",
       "         [0.0300],\n",
       "         [0.0400]]), tensor([[0.3000],\n",
       "         [0.3070],\n",
       "         [0.3140],\n",
       "         [0.3210],\n",
       "         [0.3280]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create some data (same as notebook 01)\n",
    "weight = 0.7\n",
    "bias = 0.3\n",
    "start = 0\n",
    "end = 1\n",
    "step = 0.01\n",
    "\n",
    "# Create data\n",
    "X_regression = torch.arange(start, end, step).unsqueeze(dim=1)\n",
    "y_regression = weight * X_regression + bias # linear regression formula\n",
    "\n",
    "# Check the data\n",
    "print(len(X_regression))\n",
    "X_regression[:5], y_regression[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wquTX_wX275-"
   },
   "source": [
    "Wonderful, now let's split our data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UIoO2k8yHC8D",
    "outputId": "2d9144d9-18d9-427a-a898-7a5c920cd9aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 80 20 20\n"
     ]
    }
   ],
   "source": [
    "# Create train and test splits\n",
    "train_split = int(0.8 * len(X_regression)) # 80% of data used for training set\n",
    "X_train_regression, y_train_regression = X_regression[:train_split], y_regression[:train_split]\n",
    "X_test_regression, y_test_regression = X_regression[train_split:], y_regression[train_split:]\n",
    "\n",
    "# Check the lengths of each split\n",
    "print(len(X_train_regression), \n",
    "    len(y_train_regression), \n",
    "    len(X_test_regression), \n",
    "    len(y_test_regression))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQtonoMn3s90"
   },
   "source": [
    "Beautiful, let's see how the data looks.\n",
    "\n",
    "To do so, we'll use the `plot_predictions()` function we created in notebook 01. \n",
    "\n",
    "It's contained within the [`helper_functions.py` script](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/helper_functions.py) on the Learn PyTorch for Deep Learning repo which we downloaded above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "pcg5OvncHC8D",
    "outputId": "77b50411-c589-4d5f-e7ca-d03b1b9a68cc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ppJOO+IhEom+oNDOdddZZuvXWW1VRUaG+fftqxYoVkqQuXbqovr4+6XWMGDFCr7zyij766KOmfZs2bWr3OUOGDNFRRx2ljRs3Nu07ePCgXnnllabtnTt3av/+/fre976nc845RyNGjGi6G/apLl26SFKLX8eBAwe0c+dOLVy4UBMnTtTIkSP1/vvv6/Dhw0n9OgEAqROGmxRhw2iENLjllls0ZcoUDRw4UF/+8pcViUT0yiuvaNOmTbrzzju1ceNG/fd//7cmTZqkPn36aMuWLdqzZ49GjRolKfquvT/96U966aWXdOKJJ6pHjx46+uijO7yOyy+/XDfffLOuvvpqLVy4UG+//XbTO/PMLOZzunfvrquuuko33HCDSktL1bdvXy1evLhFKDrxxBN19NFH66c//anmzp2rqqoqfec732nxOgMHDpSZ6cknn9TUqVNVVFSkXr16qaSkRPfee68GDBig6upqLViwoClgAgDCJ59GHiQq5+9MhcGkSZP05JNPat26dRo/frzGjx+vf/3Xf9WJJ54oSSouLtbzzz+vKVOmaOjQobruuuv0ne98R1/5ylckSZdeeqkuuOACff7zn1dpaakefvjhTq2jR48e+t3vfqft27frtNNO04IFC3TbbbdJkrp27drm85YsWaLzzjtPl1xyic477zyNHj1a55xzTtPjpaWluv/++/Xb3/5Wo0aN0qJFi/SDH/ygxWv069dPixYt0k033aQ+ffpo3rx5Kigo0IoVK7Rt2zaNHj1ac+fO1e23396poAgACF6+jzxIlEUb1NOvrKzMKysrYz5WVVWlkSNHpnlF+ek///M/dckll2jfvn0qKSnJ9HKSwvcNAAQrsjiieq9XoRXq8C353YJhZpvdvSzWY9yZyjP333+/nn32We3evVurV6/W/PnzNXXq1KwPUgCA4NEflRjCVJ555513dOWVV2r48OGaO3euJk+efMSUdABA/mHkQedR5kPO4PsGADqPkl77KPMBAIB2UdLrvNC+B72hoUEFBWQ9JCbVg0kBINcx8qDzQplWunXrpurqatXV1SlTZUhkB3dXXV2dqqur1a1bt0wvBwBCj3EHwQtlz1RDQ4P279+vd999l2nYiCsSiai4uFglJSXczQSAOOiN6pz2eqZCWeYrKChQ79691bt370wvBQCAnDJ73GyVby6nNypA/DceAIAcxbiD9CBMAQCQo8o3l6ve61W+uTzTS8lphCkAAHIU4w7SI5QN6AAAAGHC0E4AAHIcIw8yhzAFAEAOoD8qcxIKU2Z2vpm9ama7zOzGGI8PNLNnzGybma03s/7BLxUAALSF/qjMiRumzKxQ0lJJkyWNkjTDzEa1OmyJpH939zGSFkv6ftALBQAAUYw8CJdE7kyNl7TL3d9w9zpJj0ia1uqYUZL+p/HrdTEeBwAAAaGkFy6JhKl+kvY0297buK+5rZL+vvHrSyT1MLPPtH4hM7vGzCrNrLK2trYz6wUAIO9R0guXoBrQr5c0wcy2SJogqVpSfeuD3H2Zu5e5e1lpaWlApwYAIHdR0gu/RMJUtaQBzbb7N+5r4u5vu/vfu/tpkm5q3Pd/ga0SAIA8RUkv/BIJUxWShprZYDPrImm6pFXNDzCzEjP79LW+Lem+YJcJAEB+oqQXfglNQDezCyT9UFKhpPvc/Q4zWyyp0t1Xmdk/KPoOPpe0QdJcd/+kvddkAjoAAMgWSU9Ad/c17j7M3Ye4+x2N+25x91WNXz/u7kMbj/l6vCAFAACOxBTz7MQEdAAAQoL+qOxEmAIAICToj8pOhCkAADKAkQe5gzAFAEAGUNLLHYQpAAAygJJe7khoNEIqMBoBAABki6RHIwAAgM5j5EFuI0wBAJBi9EflNsIUAAApRn9UbiNMAQAQkLbKeYw8yG2EKQAAAkI5Lz8RpgAACAjlvPzEaAQAAIA4GI0AAEDAGHeATxGmAADoBPqj8CnCFAAAnUB/FD5FmAIAII65c6VIJPr5U4w7wKcIUwAAxFFeLtXXRz8DrRGmAACIY/ZsqbAw+hlojdEIAAAAcTAaAQCABMXqjwLaQ5gCAKAZ+qPQUYQpAACaoT8KHUWYAgDkrZgjD5ZKhw9HPwOJIEwBAPIWJT0EgTAFAMhblPQQBEYjAAAAxMFoBABA3mPkAVKFMAUAyAv0RyFVCFMAgLxAfxRShTAFAMg5jDxAOhGmAAA5h5Ie0okwBQDIOZT0kE6MRgAAAIiD0QgAgJzFyANkGmEKAJDV6I9CphGmAABZjf4oZBphCgCQFdoq5zHyAJlGmAIAZAXKeQgrwhQAICtQzkNYMRoBAAAgjqRHI5jZ+Wb2qpntMrMbYzx+opmtM7MtZrbNzC5IdtEAgPzFuANkk7h3psysUNJrkr4gaa+kCkkz3H1Hs2OWSdri7j83s1GS1rj7oPZelztTAIC2RCLR/qjCwmhzOZBpyd6ZGi9pl7u/4e51kh6RNK3VMS6pZ+PXxZLe7uxiAQCgPwrZJJEw1U/Snmbbexv3NXebpK+Y2V5JayR9I9YLmdk1ZlZpZpW1tbWdWC4AINfEKukx7gDZJKh3882QtNzd+0u6QNIDZnbEa7v7Mncvc/ey0tLSgE4NAMhmjDxAtkskTFVLGtBsu3/jvuaukvSoJLn7C5K6SioJYoEAgNxGSQ/ZLpEwVSFpqJkNNrMukqZLWtXqmLckfV6SzGykomGKOh4AIC5Kesh2ccOUux+WNE/SWklVkh519+1mttjMLmo87DpJV5vZVkkPS5rlmRpgBQAILUYeIBcxtBMAkDaMPEC2SnpoJwAAQaA/CrmIMAUASAlGHiBfEKYAACnByAPkC8IUACAlKOkhX9CADgAAEAcN6ACAlGLkAfIZYQoAkDT6o5DPCFMAgKTRH4V8RpgCAHQIIw+AlghTAIAOoaQHtESYAgB0CCU9oCVGIwAAAMTBaAQAQKcw8gCIjzAFAGgT/VFAfIQpAECb6I8C4iNMAQDaLOcx8gCIjzAFAKCcBySBMAUAoJwHJIHRCAAAAHEwGgEA0IRxB0CwCFMAkGfojwKCRZgCgDxDfxQQLMIUAOSwWCU9xh0AwSJMAUAOo6QHpB5hCgByGCU9IPUYjQAAABAHoxEAIA8w8gDIDMIUAOQI+qOAzCBMAUCOoD8KyAzCFABkIUYeAOFBmAKALERJDwgPwhQAZCFKekB4MBoBAAAgDkYjAEAWY+QBEG6EKQAIOfqjgHAjTAFAyNEfBYQbYQoAQoSRB0D2IUwBQIhQ0gOyD2EKAEKEkh6QfRiNAAAAEAejEQAghBh5AOSGhMKUmZ1vZq+a2S4zuzHG4/eY2cuNH6+Z2f8Fv1QAyC30RwG5IW6YMrNCSUslTZY0StIMMxvV/Bh3/xd3P9XdT5X0E0krU7FYAMgl9EcBuSGRO1PjJe1y9zfcvU7SI5KmtXP8DEkPB7E4AMgVjDwAclciYaqfpD3Ntvc27juCmQ2UNFjS/7Tx+DVmVmlmlbW1tR1dKwBkLUp6QO4KugF9uqTH3b0+1oPuvszdy9y9rLS0NOBTA0B4UdIDclciYapa0oBm2/0b98UyXZT4AOAIlPSA3JVImKqQNNTMBptZF0UD06rWB5nZCEm9JL0Q7BIBIHsw7gDIP3HDlLsfljRP0lpJVZIedfftZrbYzC5qduh0SY94pqaAAkAI0BsF5J9IIge5+xpJa1rtu6XV9m3BLQsAstPs2dEgRW8UkD+YgA4AncS4AwASYQoAOo2SHgCJMAUAnca4AwCSZJnqFy8rK/PKysqMnBsAAKAjzGyzu5fFeow7UwCQAEYeAGgLYQoAEkB/FIC2EKYAIAH0RwFoC2EKAFph5AGAjiBMAUArlPQAdARhCgBaoaQHoCMYjQAAABAHoxEAoA2MPACQLMIUgLxGfxSAZBGmAOQ1+qMAJIswBSBvMPIAQCoQpgDkDUp6AFKBMAUgb1DSA5AKjEYAAACIg9EIAPIOIw8ApAthCkBOoj8KQLoQpgDkJPqjAKQLYQpA1mPkAYBMIkwByHqU9ABkEmEKQNajpAcgkxiNAAAAEAejEQDkBMYdAAgjwhSArEFvFIAwIkwByBr0RgEII8IUgFBi3AGAbEGYAhBKlPQAZAvCFIBQoqQHIFswGgEAACAORiMACDVGHgDIZoQpABlHfxSAbEaYApBx9EcByGaEKQBpxcgDALmGMAUgrSjpAcg1hCkAaUVJD0CuYTQCAABAHIxGAJARjDwAkA8IUwBShv4oAPkgoTBlZueb2atmtsvMbmzjmC+b2Q4z225m/xHsMgFkI/qjAOSDuGHKzAolLZU0WdIoSTPMbFSrY4ZK+rakv3X3kyXNT8FaAYQYIw8A5KtE7kyNl7TL3d9w9zpJj0ia1uqYqyUtdfe/SJK77wt2mQDCjpIegHyVSJjqJ2lPs+29jfuaGyZpmJk9b2Ybzez8WC9kZteYWaWZVdbW1nZuxQBCiZIegHwVVAN6RNJQSedKmiHpXjM7tvVB7r7M3cvcvay0tDSgUwMIA0p6APJVImGqWtKAZtv9G/c1t1fSKnc/5O5vSnpN0XAFIAcx8gAA/iqRMFUhaaiZDTazLpKmS1rV6pjfKnpXSmZWomjZ740A1wkgROiPAoC/ihum3P2wpHmS1kqqkvSou283s8VmdlHjYWslHTCzHZLWSVrg7gdStWgAmUV/FAD8FT9OBkC75s6N3oGaPZt+KAD5ix8nA6DTKOkBQPsIUwDaRUkPANpHmQ8AACAOynwA4mLcAQB0DmEKgCR6owCgswhTACTRGwUAnUWYAvJQrJIePw4GADqHMAXkIUp6ABAcwhSQhyjpAUBwGI0AAAAQB6MRgDzGyAMASC3CFJDj6I8CgNQiTAE5jv4oAEgtwhSQQxh5AADpR5gCcgglPQBIP8IUkEMo6QFA+jEaAQAAIA5GIwA5iJEHABAOhCkgS9EfBQDhQJgCshT9UQAQDoQpIAsw8gAAwoswBWQBSnoAEF6EKSALUNIDgPBiNAIAAEAcjEYAsggjDwAguxCmgJChPwoAsgthCggZ+qMAILsQpoAMYuQBAGQ/whSQQZT0ACD7EaaADKKkBwDZj9EIAAAAcTAaAcgwxh0AQO4iTAFpQG8UAOQuwhSQBvRGAUDuIkwBAWPcAQDkF8IUEDBKegCQXwhTQMAo6QFAfmE0AgAAQByMRgBShJEHAADCFJAE+qMAAIQpIAn0RwEAEgpTZna+mb1qZrvM7MYYj88ys1oze7nx4+vBLxXILEYeAABiiduAbmaFkl6T9AVJeyVVSJrh7juaHTNLUpm7z0v0xDSgI9tEItGSXmFhNEABAPJHsg3o4yXtcvc33L1O0iOSpgW5QCAbUNIDAMSSSJjqJ2lPs+29jftau9TMtpnZ42Y2INYLmdk1ZlZpZpW1tbWdWC6QOZT0AACxBNWA/jtJg9x9jKTfS7o/1kHuvszdy9y9rLS0NKBTA8Fj5AEAIFGJhKlqSc3vNPVv3NfE3Q+4+yeNm7+UNC6Y5QGZwcgDAECiEglTFZKGmtlgM+siabqkVc0PMLMTmm1eJKkquCUC6Ud/FAAgUXHDlLsfljRP0lpFQ9Kj7r7dzBab2UWNh/2zmW03s62S/lnSrFQtGAgaIw8AAMngZ/Mh7zHyAAAQDz+bD2gHJT0AQDK4MwUAABAHd6aARow8AAAEjTCFvMLIAwBA0AhTyCv0RwEAgkaYQs5i5AEAIB0IU8hZlPQAAOlAmELOoqQHAEgHRiMAAADEwWgE5DTGHQAAMokwhaxHbxQAIJMIU8h69EYBADKJMIWswrgDAEDYEKaQVSjpAQDChjCFrEJJDwAQNoxGAAAAiIPRCMhKjDwAAGQDwhRCi/4oAEA2IEwhtOiPAgBkA8IUQoGRBwCAbEWYQihQ0gMAZCvCFEKBkh4AIFsxGgEAACAORiMgVBh5AADIJYQppB39UQCAXEKYQtrRHwUAyCWEKaQUIw8AALmOMIWUoqQHAMh1hCmkFCU9AECuYzQCAABAHIxGQFow8gAAkI8IUwgM/VEAgHxEmEJg6I8CAOQjwhQ6hZEHAABEEabQKZT0AACIIkyhUyjpAQAQxWgEAACAOBiNgE5j3AEAAO0jTKFd9EYBANA+whTaRW8UACH+KUAAAAisSURBVADtI0yhCeMOAADouITClJmdb2avmtkuM7uxneMuNTM3s5gNWgg3SnoAAHRc3DBlZoWSlkqaLGmUpBlmNirGcT0kXSvpxaAXifSgpAcAQMclcmdqvKRd7v6Gu9dJekTStBjH3S7p3yR9HOD6kEaU9AAA6LhEwlQ/SXuabe9t3NfEzMZKGuDuT7b3QmZ2jZlVmlllbW1thxeL4DDyAACAYCTdgG5mBZJ+IOm6eMe6+zJ3L3P3stLS0mRPjSTQHwUAQDASCVPVkgY02+7fuO9TPSSNlrTezHZLOlPSKprQw43+KAAAgpFImKqQNNTMBptZF0nTJa369EF3f9fdS9x9kLsPkrRR0kXuzs+KCQlGHgAAkDpxw5S7H5Y0T9JaSVWSHnX37Wa22MwuSvUCkTxKegAApE4kkYPcfY2kNa323dLGsecmvywEafbsaJCipAcAQPDM3TNy4rKyMq+spBIIAADCz8w2u3vMfnB+nEyOYeQBAADpRZjKMfRHAQCQXoSpHMPIAwAA0oswlcUYeQAAQOYRprIYJT0AADKPMJXFKOkBAJB5jEYAAACIg9EIOYCRBwAAhBNhKkvQHwUAQDgRprIE/VEAAIQTYSqEGHkAAED2IEyFECU9AACyB2EqhCjpAQCQPRiNAAAAEAejEUKKcQcAAGQ/wlQG0RsFAED2I0xlEL1RAABkP8JUmjDuAACA3ESYShNKegAA5CbCVJpQ0gMAIDcxGgEAACAORiOkGSMPAADIH4SpFKA/CgCA/EGYSgH6owAAyB+EqSQx8gAAgPxGmEoSJT0AAPIbYSpJlPQAAMhvjEYAAACIg9EIAWHkAQAAaI0w1QH0RwEAgNYIUx1AfxQAAGiNMNUGRh4AAIBEEKbaQEkPAAAkgjDVBkp6AAAgEYxGAAAAiIPRCHEw8gAAAHQWYUr0RwEAgM4jTIn+KAAA0Hl5F6YYeQAAAIKUUJgys/PN7FUz22VmN8Z4/J/M7P+Z2ctm9pyZjQp+qcGgpAcAAIIUN0yZWaGkpZImSxolaUaMsPQf7n6Ku58q6U5JPwh8pQGhpAcAAIKUyJ2p8ZJ2ufsb7l4n6RFJ05of4O7vNdvsJikz8xYSQEkPAAAEKZEw1U/Snmbbexv3tWBmc83sdUXvTP1zMMvrPMYdAACAdAisAd3dl7r7EEk3SLo51jFmdo2ZVZpZZW1tbVCnjoneKAAAkA6JhKlqSQOabfdv3NeWRyRdHOsBd1/m7mXuXlZaWpr4KjuB3igAAJAOiYSpCklDzWywmXWRNF3SquYHmNnQZpsXSvpjcEvsHHqjAABAOkTiHeDuh81snqS1kgol3efu281ssaRKd18laZ6ZTZR0SNJfJM1M5aIBAADCIm6YkiR3XyNpTat9tzT7+tqA1wUAAJAV8m4COgAAQJAIUwAAAEkgTAEAACSBMAUAAJAEwhQAAEASCFMAAABJIEwBAAAkgTAFAACQBMIUAABAEghTAAAASSBMAQAAJIEwBQAAkARz98yc2KxW0p9SfJoSSftTfA50DtcmnLgu4cW1CSeuS3gFfW0GuntprAcyFqbSwcwq3b0s0+vAkbg24cR1CS+uTThxXcIrndeGMh8AAEASCFMAAABJyPUwtSzTC0CbuDbhxHUJL65NOHFdwitt1yane6YAAABSLdfvTAEAAKQUYQoAACAJORGmzOx8M3vVzHaZ2Y0xHj/azFY0Pv6imQ1K/yrzUwLX5ptmtsPMtpnZM2Y2MBPrzDfxrkuz4y41Mzcz3vqdJolcGzP7cuOfm+1m9h/pXmM+SuDvshPNbJ2ZbWn8++yCTKwz35jZfWa2z8xeaeNxM7MfN163bWY2NhXryPowZWaFkpZKmixplKQZZjaq1WFXSfqLu58k6R5J/5beVeanBK/NFkll7j5G0uOS7kzvKvNPgtdFZtZD0rWSXkzvCvNXItfGzIZK+rakv3X3kyXNT/tC80yCf2ZulvSou58mabqkn6V3lXlruaTz23l8sqShjR/XSPp5KhaR9WFK0nhJu9z9DXevk/SIpGmtjpkm6f7Grx+X9HkzszSuMV/FvTbuvs7dP2zc3Cipf5rXmI8S+TMjSbcr+h+Pj9O5uDyXyLW5WtJSd/+LJLn7vjSvMR8lcl1cUs/Gr4slvZ3G9eUtd98g6X/bOWSapH/3qI2SjjWzE4JeRy6EqX6S9jTb3tu4L+Yx7n5Y0ruSPpOW1eW3RK5Nc1dJ+q+UrghSAtel8Vb4AHd/Mp0LQ0J/ZoZJGmZmz5vZRjNr73/lCEYi1+U2SV8xs72S1kj6RnqWhjg6+u9Qp0SCfkGgM8zsK5LKJE3I9FrynZkVSPqBpFkZXgpiiyhasjhX0Tu5G8zsFHf/v4yuCjMkLXf3u83sLEkPmNlod2/I9MKQerlwZ6pa0oBm2/0b98U8xswiit6CPZCW1eW3RK6NzGyipJskXeTun6Rpbfks3nXpIWm0pPVmtlvSmZJW0YSeFon8mdkraZW7H3L3NyW9pmi4Quokcl2ukvSoJLn7C5K6KvqDdpFZCf07lKxcCFMVkoaa2WAz66Jo49+qVseskjSz8et/kPQ/zrTSdIh7bczsNEnligYpej/So93r4u7vunuJuw9y90GK9rJd5O6VmVluXknk77PfKnpXSmZWomjZ7410LjIPJXJd3pL0eUkys5GKhqnatK4SsayS9I+N7+o7U9K77l4T9Emyvszn7ofNbJ6ktZIKJd3n7tvNbLGkSndfJelXit5y3aVoo9r0zK04fyR4be6S1F3SY43vCXjL3S/K2KLzQILXBRmQ4LVZK+mLZrZDUr2kBe7OnfYUSvC6XCfpXjP7F0Wb0Wfxn/bUM7OHFf3PRUljv9qtko6SJHf/haL9axdI2iXpQ0lfTck6uNYAAACdlwtlPgAAgIwhTAEAACSBMAUAAJAEwhQAAEASCFMAAABJIEwBAAAkgTAFAACQhP8PhFTBxnAtLrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(train_data=X_train_regression,\n",
    "    train_labels=y_train_regression,\n",
    "    test_data=X_test_regression,\n",
    "    test_labels=y_test_regression\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZRflODP66kG"
   },
   "source": [
    "### 5.2 Adjusting `model_1` to fit a straight line\n",
    "\n",
    "Now we've got some data, let's recreate `model_1` but with a loss function suited to our regression data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rc13zblAHC8D",
    "outputId": "7bd16b1f-bd2e-486b-b963-9733aaa757be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=1, out_features=10, bias=True)\n",
       "  (1): Linear(in_features=10, out_features=10, bias=True)\n",
       "  (2): Linear(in_features=10, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same architecture as model_1 (but using nn.Sequential)\n",
    "model_2 = nn.Sequential(\n",
    "    nn.Linear(in_features=1, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=10),\n",
    "    nn.Linear(in_features=10, out_features=1)\n",
    ").to(device)\n",
    "\n",
    "model_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOtBAv1E7OqX"
   },
   "source": [
    "We'll setup the loss function to be `nn.L1Loss()` (the same as mean absolute error) and the optimizer to be `torch.optim.SGD()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "f06fraapHC8E"
   },
   "outputs": [],
   "source": [
    "# Loss and optimizer\n",
    "loss_fn = nn.L1Loss()\n",
    "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lU7GfFLm7a21"
   },
   "source": [
    "Now let's train the model using the regular training loop steps for `epochs=1000` (just like `model_1`).\n",
    "\n",
    "> **Note:** We've been writing similar training loop code over and over again. I've made it that way on purpose though, to keep practicing. However, do you have ideas how we could functionize this? That would save a fair bit of coding in the future. Potentially there could be a function for training and a function for testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YTitEWgSHC8E",
    "outputId": "16da5efa-3c3b-494b-ef4e-e5244f4cf097"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.75986, Test loss: 0.54143\n",
      "Epoch: 100 | Train loss: 0.09309, Test loss: 0.02901\n",
      "Epoch: 200 | Train loss: 0.07376, Test loss: 0.02850\n",
      "Epoch: 300 | Train loss: 0.06745, Test loss: 0.00615\n",
      "Epoch: 400 | Train loss: 0.06107, Test loss: 0.02004\n",
      "Epoch: 500 | Train loss: 0.05698, Test loss: 0.01061\n",
      "Epoch: 600 | Train loss: 0.04857, Test loss: 0.01326\n",
      "Epoch: 700 | Train loss: 0.06109, Test loss: 0.02127\n",
      "Epoch: 800 | Train loss: 0.05599, Test loss: 0.01426\n",
      "Epoch: 900 | Train loss: 0.05571, Test loss: 0.00603\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set the number of epochs\n",
    "epochs = 1000\n",
    "\n",
    "# Put data to target device\n",
    "X_train_regression, y_train_regression = X_train_regression.to(device), y_train_regression.to(device)\n",
    "X_test_regression, y_test_regression = X_test_regression.to(device), y_test_regression.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training \n",
    "    # 1. Forward pass\n",
    "    y_pred = model_2(X_train_regression)\n",
    "    \n",
    "    # 2. Calculate loss (no accuracy since it's a regression problem, not classification)\n",
    "    loss = loss_fn(y_pred, y_train_regression)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_2.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_pred = model_2(X_test_regression)\n",
    "      # 2. Calculate the loss \n",
    "      test_loss = loss_fn(test_pred, y_test_regression)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0: \n",
    "        print(f\"Epoch: {epoch} | Train loss: {loss:.5f}, Test loss: {test_loss:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IoyLsZW78m_6"
   },
   "source": [
    "Okay, unlike `model_1` on the classification data, it looks like `model_2`'s loss is actually going down.\n",
    "\n",
    "Let's plot its predictions to see if that's so.\n",
    "\n",
    "And remember, since our model and data are using the target `device`, and this device may be a GPU, however, our plotting function uses matplotlib and matplotlib can't handle data on the GPU.\n",
    "\n",
    "To handle that, we'll send all of our data to the CPU using [`.cpu()`](https://pytorch.org/docs/stable/generated/torch.Tensor.cpu.html) when we pass it to `plot_predictions()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "id": "AvltMCW_HC8E",
    "outputId": "5887db7d-128b-46f3-c978-c12af112d470"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Turn on evaluation mode\n",
    "model_2.eval()\n",
    "\n",
    "# Make predictions (inference)\n",
    "with torch.inference_mode():\n",
    "    y_preds = model_2(X_test_regression)\n",
    "\n",
    "# Plot data and predictions with data on the CPU (matplotlib can't handle data on the GPU)\n",
    "# (try removing .cpu() from one of the below and see what happens)\n",
    "plot_predictions(train_data=X_train_regression.cpu(),\n",
    "                 train_labels=y_train_regression.cpu(),\n",
    "                 test_data=X_test_regression.cpu(),\n",
    "                 test_labels=y_test_regression.cpu(),\n",
    "                 predictions=y_preds.cpu());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cZFiXR8B9wYx"
   },
   "source": [
    "Alright, it looks like our model is able to do far better than random guessing on straight lines.\n",
    "\n",
    "This is a good thing.\n",
    "\n",
    "It means our model at least has *some* capacity to learn.\n",
    "\n",
    "> **Note:** A helpful troubleshooting step when building deep learning models is to start as small as possible to see if the model works before scaling it up. \n",
    ">\n",
    "> This could mean starting with a simple neural network (not many layers, not many hidden neurons) and a small dataset (like the one we've made) and then **overfitting** (making the model perform too well) on that small example before increasing the amount data or the model size/design to *reduce* overfitting.\n",
    "\n",
    "So what could it be?\n",
    "\n",
    "Let's find out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j82n3OyWHC8E"
   },
   "source": [
    "## 6. The missing piece: non-linearity \n",
    "\n",
    "We've seen our model can draw straight (linear) lines, thanks to its linear layers.\n",
    "\n",
    "But how about we give it the capacity to draw non-straight (non-linear) lines?\n",
    "\n",
    "How?\n",
    "\n",
    "Let's find out.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cmfOV8v6__17"
   },
   "source": [
    "### 6.1 Recreating non-linear data (red and blue circles)\n",
    "\n",
    "First, let's recreate the data to start off fresh. We'll use the same setup as before. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "owqilKGBHC8F",
    "outputId": "b8c1d692-6f3e-43ca-9323-98bd40e39a89"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make and plot data\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "n_samples = 1000\n",
    "\n",
    "X, y = make_circles(n_samples=1000,\n",
    "    noise=0.03,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D63mlmxR_pxt"
   },
   "source": [
    "Nice! Now let's split it into training and test sets using 80% of the data for training and 20% for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0UkH4cKLHC8F",
    "outputId": "5619ad2f-ad5d-476a-dcfd-9f8d48036899"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[ 0.6579, -0.4651],\n",
       "         [ 0.6319, -0.7347],\n",
       "         [-1.0086, -0.1240],\n",
       "         [-0.9666, -0.2256],\n",
       "         [-0.1666,  0.7994]]), tensor([1., 0., 0., 0., 1.]))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to tensors and split into train and test sets\n",
    "import torch\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Turn data into tensors\n",
    "X = torch.from_numpy(X).type(torch.float)\n",
    "y = torch.from_numpy(y).type(torch.float)\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42\n",
    ")\n",
    "\n",
    "X_train[:5], y_train[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wNm_OBgD_4tk"
   },
   "source": [
    "### 6.2 Building a model with non-linearity \n",
    "\n",
    "Now here comes the fun part.\n",
    "\n",
    "What kind of pattern do you think you could draw with unlimited straight (linear) and non-straight (non-linear) lines?\n",
    "\n",
    "I bet you could get pretty creative.\n",
    "\n",
    "So far our neural networks have only been using linear (straight) line functions.\n",
    "\n",
    "But the data we've been working with is non-linear (circles).\n",
    "\n",
    "What do you think will happen when we introduce the capability for our model to use **non-linear actviation functions**?\n",
    "\n",
    "Well let's see.\n",
    "\n",
    "PyTorch has a bunch of [ready-made non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that do similiar but different things. \n",
    "\n",
    "One of the most common and best performing is [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) (rectified linear-unit, [`torch.nn.ReLU()`](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html)).\n",
    "\n",
    "Rather than talk about it, let's put it in our neural network between the hidden layers in the forward pass and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i_yG7AFHHC8F",
    "outputId": "00a69e38-369f-47fb-b729-fd4c7b5b47de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CircleModelV2(\n",
      "  (layer_1): Linear(in_features=2, out_features=10, bias=True)\n",
      "  (layer_2): Linear(in_features=10, out_features=10, bias=True)\n",
      "  (layer_3): Linear(in_features=10, out_features=1, bias=True)\n",
      "  (relu): ReLU()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Build model with non-linear activation function\n",
    "from torch import nn\n",
    "class CircleModelV2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.layer_1 = nn.Linear(in_features=2, out_features=10)\n",
    "        self.layer_2 = nn.Linear(in_features=10, out_features=10)\n",
    "        self.layer_3 = nn.Linear(in_features=10, out_features=1)\n",
    "        self.relu = nn.ReLU() # <- add in ReLU activation function\n",
    "        # Can also put sigmoid in the model \n",
    "        # This would mean you don't need to use it on the predictions\n",
    "        # self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "    def forward(self, x):\n",
    "      # Intersperse the ReLU activation function between layers\n",
    "       return self.layer_3(self.relu(self.layer_2(self.relu(self.layer_1(x)))))\n",
    "\n",
    "model_3 = CircleModelV2().to(device)\n",
    "print(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UASf5SWEPNJ"
   },
   "source": [
    "![a classification neural network on TensorFlow playground with ReLU activation](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-tensorflow-playground-relu-activation.png)\n",
    "*A visual example of what a similar classificiation neural network to the one we've just built (suing ReLU activation) looks like. Try create one of your own on the [TensorFlow Playground website](https://playground.tensorflow.org/).*\n",
    "\n",
    "> **Question:** *Where should I put the non-linear activation functions when constructing a neural network?*\n",
    ">\n",
    "> A rule of thumb is to put them in between hidden layers and just after the output layer, however, there is no set in stone option. As you learn more about neural networks and deep learning you'll find a bunch of different ways of putting things together. In the meantine, best to experiment, experiment, experiment.\n",
    "\n",
    "Now we've got a model ready to go, let's create a binary classification loss function as well as an optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "uWNlx4lTHC8F"
   },
   "outputs": [],
   "source": [
    "# Setup loss and optimizer \n",
    "loss_fn = nn.BCEWithLogitsLoss()\n",
    "optimizer = torch.optim.SGD(model_3.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQL9GF5yFTGD"
   },
   "source": [
    "Wonderful! \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "INnCmr2RMk8L"
   },
   "source": [
    "### 6.3 Training a model with non-linearity\n",
    "\n",
    "You know the drill, model, loss function, optimizer ready to go, let's create a training and testing loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gTS8yTN_HC8F",
    "outputId": "df71bd5d-cd0d-4e39-f3e0-7fc90bfaaeb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.69295, Accuracy: 50.00% | Test Loss: 0.69319, Test Accuracy: 50.00%\n",
      "Epoch: 100 | Loss: 0.69115, Accuracy: 52.88% | Test Loss: 0.69102, Test Accuracy: 52.50%\n",
      "Epoch: 200 | Loss: 0.68977, Accuracy: 53.37% | Test Loss: 0.68940, Test Accuracy: 55.00%\n",
      "Epoch: 300 | Loss: 0.68795, Accuracy: 53.00% | Test Loss: 0.68723, Test Accuracy: 56.00%\n",
      "Epoch: 400 | Loss: 0.68517, Accuracy: 52.75% | Test Loss: 0.68411, Test Accuracy: 56.50%\n",
      "Epoch: 500 | Loss: 0.68102, Accuracy: 52.75% | Test Loss: 0.67941, Test Accuracy: 56.50%\n",
      "Epoch: 600 | Loss: 0.67515, Accuracy: 54.50% | Test Loss: 0.67285, Test Accuracy: 56.00%\n",
      "Epoch: 700 | Loss: 0.66659, Accuracy: 58.38% | Test Loss: 0.66322, Test Accuracy: 59.00%\n",
      "Epoch: 800 | Loss: 0.65160, Accuracy: 64.00% | Test Loss: 0.64757, Test Accuracy: 67.50%\n",
      "Epoch: 900 | Loss: 0.62362, Accuracy: 74.00% | Test Loss: 0.62145, Test Accuracy: 79.00%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "epochs = 1000\n",
    "\n",
    "# Put all data on target device\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_3(X_train).squeeze()\n",
    "    y_pred = torch.round(torch.sigmoid(y_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "    \n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_train) # BCEWithLogitsLoss calculates loss using logits\n",
    "    acc = accuracy_fn(y_true=y_train, \n",
    "                      y_pred=y_pred)\n",
    "    \n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backward\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_3.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_3(X_test).squeeze()\n",
    "      test_pred = torch.round(torch.sigmoid(test_logits)) # logits -> prediction probabilities -> prediction labels\n",
    "      # 2. Calcuate loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_test)\n",
    "      test_acc = accuracy_fn(y_true=y_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 100 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Accuracy: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Accuracy: {test_acc:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x89XvV-EMqvB"
   },
   "source": [
    "Ho ho! That's looking far better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tfViHC1aM15t"
   },
   "source": [
    "### 6.4 Evaluating a model trained with non-linear activation functions\n",
    "\n",
    "Remember how our circle data is non-linear? Well, let's see how our models predictions look now the model's been trained with non-linear activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wGHQiWT2HC8G",
    "outputId": "e95a412b-bef1-4d3d-b705-1eb75a55449e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([1., 0., 1., 0., 0., 1., 0., 0., 1., 0.], device='cuda:0'),\n",
       " tensor([1., 1., 1., 1., 0., 1., 1., 1., 1., 0.]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_3.eval()\n",
    "with torch.inference_mode():\n",
    "    y_preds = torch.round(torch.sigmoid(model_3(X_test))).squeeze()\n",
    "y_preds[:10], y[:10] # want preds in same format as truth labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "gEeMinjyHC8G",
    "outputId": "3566e274-ef7a-4eb8-ef34-57b97e7781bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot decision boundaries for training and test sets\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_1, X_train, y_train) # model_1 = no non-linearity\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_3, X_test, y_test) # model_3 = has non-linearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cAfU5dXrNGuC"
   },
   "source": [
    "Nice! Not perfect but still far better than before.\n",
    "\n",
    "Potentially you could try a few tricks to improve the test accuracy of the model? (hint: head back to section 5 for tips on improving the model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rarkXnX-Nhj2"
   },
   "source": [
    "## 7. Replicating non-linear activation functions\n",
    "\n",
    "We saw before how adding non-linear activation functions to our model can helped it to model non-linear data.\n",
    "\n",
    "> **Note:** Much of the data you'll encounter in the wild is non-linear (or a combination of linear and non-linear). Right now we've been working with dots on a 2D plot. But imagine if you had images of plants you'd like to classify, there's a lot of different plant shapes. Or text from Wikipedia you'd like to summarize, there's lots of different ways words can be put together (linear and non-linear patterns). \n",
    "\n",
    "But what does a non-linear activation *look* like?\n",
    "\n",
    "How about we replicate some and what they do?\n",
    "\n",
    "Let's start by creating a small amount of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uvpqD28OscTm",
    "outputId": "befa3534-f5b1-48f2-88c5-63836759d834"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
       "          2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a toy tensor (similar to the data going into our model(s))\n",
    "A = torch.arange(-10, 10, 1, dtype=torch.float32)\n",
    "A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vvSZ4M3-ssZn"
   },
   "source": [
    "Wonderful, now let's plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "FTCID1lRsrte",
    "outputId": "477fdd3d-ae28-4caf-b9ed-b2a9ec369bee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the toy tensor\n",
    "plt.plot(A);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ovyXy-Qxsx9x"
   },
   "source": [
    "A straight line, nice.\n",
    "\n",
    "Now let's see how the ReLU activation function influences it.\n",
    "\n",
    "And instead of using PyTorch's ReLU (`torch.nn.ReLU`), we'll recreate it ourselves.\n",
    "\n",
    "The ReLU function turns all negatives to 0 and leaves the positive values as they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XhdCQKbcsxi1",
    "outputId": "47c7fa54-6d03-47b7-a745-0bb3599c82fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 2., 3., 4., 5., 6., 7.,\n",
       "        8., 9.])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create ReLU function by hand \n",
    "def relu(x):\n",
    "  return torch.maximum(torch.tensor(0), x) # inputs must be tensors\n",
    "\n",
    "# Pass toy tensor through ReLU function\n",
    "relu(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dqw_P9GiuiB9"
   },
   "source": [
    "It looks like our ReLU function worked, all of the negative values are zeros.\n",
    "\n",
    "Let's plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "fICVVmAgsxal",
    "outputId": "4ada6523-81e8-450e-89b5-0be3da23f04c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ReLU activated toy tensor\n",
    "plt.plot(relu(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlmI4CHzsxEt"
   },
   "source": [
    "Nice! That looks exactly like the shape of the ReLU function on the [Wikipedia page for ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)).\n",
    "\n",
    "How about we try the [sigmoid function](https://en.wikipedia.org/wiki/Sigmoid_function) we've been using?\n",
    "\n",
    "The sigmoid function formula goes like so:\n",
    "\n",
    "$$ out_i = \\frac{1}{1+e^{-input_i}} $$ \n",
    "\n",
    "Or using $x$ as input:\n",
    "\n",
    "$$ S(x) = \\frac{1}{1+e^{-x_i}} $$\n",
    "\n",
    "Where $S$ stands for sigmoid, $e$ stands for [exponential](https://en.wikipedia.org/wiki/Exponential_function) ([`torch.exp()`](https://pytorch.org/docs/stable/generated/torch.exp.html)) and $i$ stands for a particular element in a tensor.\n",
    "\n",
    "Let's build a function to replicate the sigmoid function with PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hcDQDy8bvDcR",
    "outputId": "899e7595-5b1c-4182-c1ca-94aadaa097e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.5398e-05, 1.2339e-04, 3.3535e-04, 9.1105e-04, 2.4726e-03, 6.6929e-03,\n",
       "        1.7986e-02, 4.7426e-02, 1.1920e-01, 2.6894e-01, 5.0000e-01, 7.3106e-01,\n",
       "        8.8080e-01, 9.5257e-01, 9.8201e-01, 9.9331e-01, 9.9753e-01, 9.9909e-01,\n",
       "        9.9966e-01, 9.9988e-01])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a custom sigmoid function\n",
    "def sigmoid(x):\n",
    "  return 1 / (1 + torch.exp(-x))\n",
    "\n",
    "# Test custom sigmoid on toy tensor\n",
    "sigmoid(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qiwvlDWmxPUt"
   },
   "source": [
    "Woah, those values look a lot like prediction probabilities we've seen earlier, let's see what they look like visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "dxihhxGBxOWf",
    "outputId": "c6a6d3de-e9fb-445d-8d63-3964753a4559"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfG0lEQVR4nO3deXSc5Xn38e+l3ZK8S16QLQuDF4zBS4SBpEkgjsG4xA7QN4EshYSEkyZkKU1SetrSnKQ9DUmaNklJWrJB8iYQ2gSsJAazBF6yAXaMvGLZsvGixbYkr5KsZTTX+8eMzCAka2zPzDMz+n3OmTPPco/mOo+e+Wl0P8tt7o6IiGS+nKALEBGRxFCgi4hkCQW6iEiWUKCLiGQJBbqISJbIC+qNy8rKvKqqKqi3FxHJSH/6059a3b18sHWBBXpVVRXr168P6u1FRDKSme0dap26XEREsoQCXUQkSyjQRUSyhAJdRCRLKNBFRLLEsIFuZj8ws0NmtmWI9WZm3zSzejPbZGaLE1+miIgMJ55v6A8Ay0+z/jpgVvRxB/Cdcy9LRETO1LDnobv782ZWdZomq4AfeeQ+vC+Y2Tgzm+ruzQmqUUQEAHenqzdMTyhMKBwmFHZ6+8KE+pxQOExvnxPqc3rD0WV9YXrD0ec+py8caRd2JxyGsDvu4Dhhj8yHPfI+PmD+telI+9g7j/e3B3CImX798v6ZpRdNZsH0cQnfPom4sKgC2B8z3xBd9oZAN7M7iHyLp7KyMgFvLSKZ5FhnL/uPdHL8ZC/t3SHau0N0dIc4EX3u6O7jRFdkOnZ97HQ4w4dwMINJY4rSNtDj5u73A/cDVFdXZ/ivRUQGcnda23vY29bBnrZO9kWf97Z1sPdwJ0c7e4d8bW6OUVqYR2lhHiWFuZQW5jG6KI+pY4uiyyLrigtzKcjNIT83h7xcIz8n8pyXm0N+TuQ5dnl+rpHX3yb6nGtGjhlmkYDNic7nGFh0eex8TnTeDIzIM3Bq/rXp/uUWMx2ZT4VEBHojMD1mflp0mYhkoXDYaT7exd7WSEjvaetgX1vnqeDu7Ok71TbHoGL8KKomlnD9pVOZMaGE6ROKGVecfyq8S4siz4V5OSkLvmyViECvAe40s4eBy4Fj6j8XyS6hvjC/39XGoxsaeHLbwdeFdkFuDtMmREL7ipkTmDGhmBllJVRNLKFi3CgK8nR2dKoMG+hm9hBwFVBmZg3APwH5AO7+X8AaYAVQD3QCH0pWsSKSOu7OtubjPLqhkdUbm2g50c2YojxWLTyP+RVjqZpYwoyJxUwdO4rcHH2zTgfxnOVyyzDrHfhEwioSkUA1HzvJ6tomHt3QSN3BE+TnGlfPmcSNiyu4eu4kCvNygy5RhhDY7XNFJH20d4d4YssBHn25gT/sasMdFleO40vvns/1l0xlfElB0CVKHBToIiNUqC/M7+pbefTlRtZuPUBXb5jKCcV86h2zuGFRBVVlJUGXKGdIgS4ywmxrOs7PNzSwuraJ1vZuxo7K56bF07hxcQWLK8frTJMMpkAXGSHcnW/9pp6vP7WD/FzjHXMnccOiaVw9t1z94llCgS4yAoT6wvzj6i089NJ+blxUwT3vmse4YvWLZxsFukiW6+wJcedPX+Y32w/xiasv4LPXzFG3SpZSoItksdb2bm5/YB2bG4/xz++ezweumBF0SZJECnSRLLWntYNbf/gSB4938d8frGbZvMlBlyRJpkAXyUIv7zvC7Q+uB+CnH72CxZXjA65IUkGBLpJlnt52kDsf2sCk0UU8+OElnK/zyUcMBbpIFvnJi3v5x8e2ML9iLN+/9TLKRxcGXZKkkAJdJAu4O//25A7+89l6rp5Tzn++bzElhfp4jzT6jYtkuN6+MHf/fDM/39DAe6un8y83zCcvV7esHYkU6CIZrL07xF/93z/x252tfOads/j00lk6x3wEU6CLZKhDx7v40APr2H7gBF+56VLec9n04V8kWU2BLpKB6g+1c+sPXuJIZw/fu7Waq+dMCrokSQMKdJEMs37PYT7yo/Xk5RgP33EFl05L/OjxkpkU6CIZ5JXm47z/ey9y3rhRPPihJVROLA66JEkjCnSRDPKvj2+nKD+X//nYlZSV6hxzeT2d2ySSIX5f38rzO1q48+oLFeYyKAW6SAYIh50vP76dinGj+OCVumOiDE6BLpIBfrW5mc2Nx7hr2WyK8jW6kAxOgS6S5npCYb62to65U0bz7kUVQZcjaUyBLpLmfvriXvYd7uRvr5tLbo6uApWhKdBF0tiJrl6++Zt6rpw5katmlwddjqQ5BbpIGvvu87s53NHD3dfN1T1aZFgKdJE0dehEF9/97av8+aVTWTBdV4PK8BToImnqG0/vpLcvzOeumRN0KZIhFOgiaWh3SzsPr9vP+y6vpEpDyEmcFOgiaeira+soysvhk++YFXQpkkEU6CJpZsO+Izy+5QAffdtMjQkqZySuQDez5WZWZ2b1Znb3IOsrzexZM3vZzDaZ2YrElyqS/dwjl/iXlRbwkbfODLocyTDDBrqZ5QL3AdcB84BbzGzegGb/ADzi7ouAm4FvJ7pQkZHg2bpDvPTqYT69dBalGuRZzlA839CXAPXuvtvde4CHgVUD2jgwJjo9FmhKXIkiI0Nf2Ln38TqqJhZz85LKoMuRDBRPoFcA+2PmG6LLYn0B+ICZNQBrgE8O9oPM7A4zW29m61taWs6iXJHs9YsNDdQdPMHnrp1Lfq4Ob8mZS9RecwvwgLtPA1YAPzazN/xsd7/f3avdvbq8XJcxi/Tr6u3j60/tYMH0cay4ZErQ5UiGiifQG4HY4cSnRZfFuh14BMDd/wgUAWWJKFBkJHjwD3toPtbF3ct1ib+cvXgCfR0wy8zON7MCIgc9awa02QcsBTCzi4gEuvpUROJwtLOH+56t5+o55Vx5wcSgy5EMNmygu3sIuBNYC7xC5GyWrWb2RTNbGW32N8BHzWwj8BBwm7t7sooWySbfeW4XJ7pDfH753KBLkQwX13lR7r6GyMHO2GX3xExvA96S2NJEsl/j0ZP88A97uHHRNC6aOmb4F4ichg6liwTo35/aAcBd18wOuBLJBgp0kYBsP3Ccn29o4LY3V1ExblTQ5UgWUKCLBOTex7czujCPj191QdClSJZQoIsE4I+72ni2roWPX30h44oLgi5HsoQCXSTF3J0vP7GdqWOLuO3NVUGXI1lEgS6SYo9vOcDG/Uf562WzKcrPDbocySIKdJEU6u0L89W1dcyeXMpNi6cFXY5kGQW6SAo9ufUgr7Z28Nlr5pCbo0v8JbEU6CIptLq2kUmjC1l60eSgS5EspEAXSZFjJ3t5rq6F6y89T9/OJSkU6CIpsnbLAXr6wqxaeF7QpUiWUqCLpMjqjY1UTSzm0mljgy5FspQCXSQFDh3v4g+72li5sEL3O5ekUaCLpMCvNjXjDisXqLtFkkeBLpICqzc2cfF5Y7hwUmnQpUgWU6CLJNme1g427j+qg6GSdAp0kSSr2diEGbxL3S2SZAp0kSRyd1bXNrKkagJTx+qe55JcCnSRJNrWfJxdLR2sVHeLpIACXSSJamqbyMsxVsyfGnQpMgIo0EWSJBx2ajY28fbZ5Ywv0SAWknwKdJEkWbfnMM3HutTdIimjQBdJkpqNTYzKz2XZPN1ZUVJDgS6SBD2hML/e3MyyeZMpLsgLuhwZIRToIknwu/oWjnb26mIiSSkFukgSrK5tYlxxPm+dVR50KTKCKNBFEqyzJ8RT2w6y4pKpFOTpIyapo71NJMGefuUQnT19urOipJwCXSTBamobmTKmiCVVE4IuRUYYBbpIAh3p6OG5uhZWLjyPHI0bKikWV6Cb2XIzqzOzejO7e4g27zGzbWa21cx+mtgyRTLD41sOEAq7ulskEMOeIGtmucB9wDKgAVhnZjXuvi2mzSzg74C3uPsRM5uUrIJF0lnNxkZmlpdw8Xljgi5FRqB4vqEvAerdfbe79wAPA6sGtPkocJ+7HwFw90OJLVMk/TUfO8mLrx5m1QKNGyrBiCfQK4D9MfMN0WWxZgOzzez3ZvaCmS0f7AeZ2R1mtt7M1re0tJxdxSJp6lcbo+OG6mIiCUiiDormAbOAq4BbgO+a2biBjdz9fnevdvfq8nJdcCHZZfXGRhZMG8v5ZSVBlyIjVDyB3ghMj5mfFl0WqwGocfded38V2EEk4EVGhF0t7WxpPM7KhQP/eRVJnXgCfR0wy8zON7MC4GagZkCbx4h8O8fMyoh0wexOYJ0iaa2mNjJu6PWXaiALCc6wge7uIeBOYC3wCvCIu281sy+a2cpos7VAm5ltA54FPufubckqWiSduEcGsrhy5kQmjykKuhwZweK6r6e7rwHWDFh2T8y0A3dFHyIjyubGY7za2sHH3j4z6FJkhNOVoiLnaHVtEwW5OSy/WN0tEiwFusg56As7v9rUxNvnlDO2OD/ocmSEU6CLnIMXX23j4PFuDWQhaUGBLnIOamqbKCnIZelcjRsqwVOgi5yl7lAfazY3c+3FUxhVkBt0OSIKdJGz9fyOVo53hXSpv6QNBbrIWVpd28iEkgLecmFZ0KWIAAp0kbPS3h3i6VcO8ueXTCU/Vx8jSQ/aE0XOwlPbDtDVG9bZLZJWFOgiZ6GmtomKcaNYXDk+6FJETlGgi5yhtvZunt/ZyrsWaNxQSS8KdJEztGbLAfrCru4WSTsKdJEzVFPbyOzJpcydMjroUkReR4EucgYaj55k3Z4jrFqocUMl/SjQRc7ALzc2AfCuS9XdIulHgS5yBlbXNrGochyVE4uDLkXkDRToInHacfAErzQfZ9UCfTuX9KRAF4lTTW0TOQZ/ru4WSVMKdJE49I8b+pYLyygfXRh0OSKDUqCLxKF2/1H2He5kpbpbJI0p0EXisLq2iYK8HK6dPyXoUkSGpEAXGUaoL8yvNjWzdO4kxhRp3FBJXwp0kWG8sPswre0aN1TSnwJdZBiraxsZXZjHVXMmBV2KyGkp0EVOo6u3jye2HODa+VMoyte4oZLeFOgip/Fc3SFOdIfU3SIZQYEuchqra5soKy3kypkTgy5FZFgKdJEhnOjq5Znth7j+0qnkadxQyQDaS0WGsHbrQXpCYVaqu0UyhAJdZAiraxuZPmEUi6aPC7oUkbgo0EUG0XKim9/Xt7JqgQaykMwRV6Cb2XIzqzOzejO7+zTtbjIzN7PqxJUoknprNjcTdnR2i2SUYQPdzHKB+4DrgHnALWY2b5B2o4FPAy8mukiRVFtd28jcKaOZNVnjhkrmiOcb+hKg3t13u3sP8DCwapB2XwLuBboSWJ9Iyu1r62TDvqOsWlgRdCkiZySeQK8A9sfMN0SXnWJmi4Hp7v7r0/0gM7vDzNab2fqWlpYzLlYkFX65KTpu6IKpAVcicmbO+aComeUAXwf+Zri27n6/u1e7e3V5efm5vrVIUqyubeSyqvFMG69xQyWzxBPojcD0mPlp0WX9RgPzgefMbA9wBVCjA6OSibYfOM6Og+0ayEIyUjyBvg6YZWbnm1kBcDNQ07/S3Y+5e5m7V7l7FfACsNLd1yelYpEkWl3bRG6OseISdbdI5hk20N09BNwJrAVeAR5x961m9kUzW5nsAkVSJRx2amqbeOusMiaWatxQyTx58TRy9zXAmgHL7hmi7VXnXpZI6m3Yd4TGoyf57LWzgy5F5KzoSlGRqJqNTRTl57BsnsYNlcykQBcBevvC/HpTM0svmkxpYVz/uIqkHQW6CPD7+lbaOnpYpbNbJIMp0EWAmtomxhTl8fY5uj5CMpcCXUa8kz19rN16gBWXTKUwT+OGSuZSoMuI95vth+jo6dPFRJLxFOgy4q2ubWTS6EIu17ihkuEU6DKiHevs5bm6Ft614DxyczSQhWQ2BbqMaE9sbaanL6yBLCQrKNBlRKvZ2ETVxGIuqRgbdCki50yBLiPWoeNd/GFXGysXatxQyQ4KdBmxfrmpGXd0dotkDQW6jFg1tY3MrxjDhZNKgy5FJCEU6DIivdrawcaGY6xaoHFDJXso0GVE+uXGJszgeo0bKllEgS4jjrvzWG0jS6omMHXsqKDLEUkYBbqMOFubjrO7pYNVC9XdItlFgS4jTs3GJvJzjevmayALyS4KdBlRwmHnlxubeNuscsaXFARdjkhCKdBlRPldfSvNx7pYqUv9JQsp0GXEcHe+9mQd540t4tqL1d0i2UeBLiPGrzc3s6nhGHddM4eifA1kIdlHgS4jQm9fmK+urWPulNHcsEhnt0h2UqDLiPDQS/vY29bJ3y6fq/ueS9ZSoEvWa+8O8c1ndnL5+RO4SoNASxZToEvW++7zu2lt7+HvVlyk2+RKVlOgS1ZrOdHNd3+7mxWXTGHh9HFBlyOSVAp0yWrffGYnPaEwn7t2btCliCSdAl2y1qutHTz00j5uWVLJ+WUlQZcjknQKdMlaX1tbR0FeDp9aOivoUkRSIq5AN7PlZlZnZvVmdvcg6+8ys21mtsnMnjGzGYkvVSR+tfuP8uvNzXz0rTMpH10YdDkiKTFsoJtZLnAfcB0wD7jFzOYNaPYyUO3ulwL/C3wl0YWKxMvd+fLjr1BWWsBH3zYz6HJEUiaeb+hLgHp33+3uPcDDwKrYBu7+rLt3RmdfAKYltkyR+D23o4UXdh/mU0tnUVqYF3Q5IikTT6BXAPtj5huiy4ZyO/D4YCvM7A4zW29m61taWuKvUiROfWHn3se3M2NiMTdfVhl0OSIpldCDomb2AaAa+Opg6939fnevdvfq8nJdsSeJ9+jLjWw/cILPXTuHgjwd85eRJZ7/RxuB6THz06LLXsfM3gn8PfB2d+9OTHki8evq7ePrT9Zx6bSxrJivwZ9l5InnK8w6YJaZnW9mBcDNQE1sAzNbBPw3sNLdDyW+TJHh/eiPe2g61sXd180lRzfgkhFo2EB39xBwJ7AWeAV4xN23mtkXzWxltNlXgVLgf8ys1sxqhvhxIklxrLOX+57dxdtnl/PmC8qCLkckEHGdAuDua4A1A5bdEzP9zgTXJXJGvv3/6jne1cvfLtcl/jJy6aiRZLymoyf54e/3cMPCCuadNybockQCo0CXjPfvT+0Ah7uumR10KSKBUqBLRqs7cIKfb2jgL6+cwbTxxUGXIxIoBbpktK88sZ2Swjw+cfWFQZciEjgFumSsF3e38cz2Q/zVVRcwvqQg6HJEAqdAl4zk7nz5ie1MGVPEh99yftDliKQFBbpkpLVbD/DyvqP89bJZFOXnBl2OSFpQoEvG6e0L85Un6pg1qZSbFuvGniL9FOiScR5Zv5/drR18fvlc8nK1C4v006dBMkpHd4j/eHonl1WN550XTQq6HJG0okCXjHG4o4cPfv9FWtu7ufu6izDTDbhEYmk4F8kI+9o6ue2HL9Fw9CTfft9i3jRjfNAliaQdBbqkvc0Nx/jQAy/R2+f89COXU101IeiSRNKSAl3S2rN1h/jETzYwvriAh+9YwoWTSoMuSSRtKdAlbT2ybj9/9+hm5k4ZzQ9vu4xJY4qCLkkkrSnQJe24O994Zif/8fRO3jqrjO984E2UFmpXFRmOPiWSVkJ9Yf7hsS08vG4/Ny2expdvuoR8nWsuEhcFuqSNju4Qd/50A8/WtfDJd1zIXctm69REkTOgQJe00HKim9sfXMeWxmP8yw3zef/lM4IuSSTjKNAlcLtb2rnth+s4dKKL+z9YzTvnTQ66JJGMpECXQG3Yd4TbH1iHmfHwHVeycPq4oEsSyVgKdAnMU9sO8smHNjB5TBEPfmgJVWUlQZckktEU6BKIH7+wl39avYVLKsby/dsuo6y0MOiSRDKeAl1SKtQX5utP7eDbz+1i6dxJfOt9iygu0G4okgj6JEnSuTtbm47z6MuN1GxsouVEN7csqeRLqy7W/cxFEkiBLknTfOwkq2ubeHRDI3UHT5Cfa1w9ZxJ/8aZpLJs3WeeYiySYAl0Sqr07xBNbDvDoyw38YVcb7rC4chxfevd8rr9kKuNLCoIuUSRrKdDlnIX6wvy2vpXHXm5k7dYDdPWGqZxQzKfeMYsbFlXo7BWRFFGgy1np7xf/xYZIv3hrezdjR+Vz0+Jp3Li4gsWV49WlIpJiCnSJS1/YaTp6kr1tnWxsOMpjLzey81A7+bnGO+ZO4oZF07h6bjmFeblBlyoyYinQ5ZSeUJiGI53sbetkT1sHe9s62Rt93n+kk94+P9X2TTPG88/vns/1l05lXLH6xUXSQVyBbmbLgW8AucD33P3LA9YXAj8C3gS0Ae919z2JLVXORTjsdPSE6Oju4+jJnteFdX+ANx09Sfi1zKakIJcZE0uYM2U011w8haqJxVROLOaC8lIma7AJkbQzbKCbWS5wH7AMaADWmVmNu2+LaXY7cMTdLzSzm4F7gfcmo+BM5+6EHXr7woTCTqgvTG+f0xf2NywLhaPP0eW9fWFCfZHn9u4Q7d0hOrpDtHf30d7dS0d3X2R5V4iOntBr090hOnr6Bq1nfHE+lRNLeNOM8dy4eBozJhRTVVZM5YQSykoL1A8ukkHi+Ya+BKh3990AZvYwsAqIDfRVwBei0/8L/KeZmbs7CfbIuv3c/9vdp+YHvsWgb+hvnO1/XWS6f7m/Nn3qefB2YY+sc4dwNKTD0XkfMB92x3ltPtEKcnMoLcqjpDCXkoI8RhflMaGkgOkTihldmEdJYR6l0UdJYR5jR+UzfcIoZkwoYWxxfuILEpFAxBPoFcD+mPkG4PKh2rh7yMyOAROB1thGZnYHcAdAZWXlWRU8vqSAOZNHv36hnXa2/73f0KZ/kcWst5gfYBhmr/28yHRkLicnsi7HIMeMHLNT63Msdn3kFZE2kffJMSMv18jPNfJycsiLee5flv+6ZTnk5Rh5uZHl+bk5p8K5pDBXByJFBEjxQVF3vx+4H6C6uvqsvqsumzeZZbpftojIG8RzI41GYHrM/LToskHbmFkeMJbIwVEREUmReAJ9HTDLzM43swLgZqBmQJsa4Nbo9F8Av0lG/7mIiAxt2C6XaJ/4ncBaIqct/sDdt5rZF4H17l4DfB/4sZnVA4eJhL6IiKRQXH3o7r4GWDNg2T0x013A/0lsaSIiciZ0M2oRkSyhQBcRyRIKdBGRLKFAFxHJEhbU2YVm1gLsPcuXlzHgKtQ0o/rOjeo7d+leo+o7ezPcvXywFYEF+rkws/XuXh10HUNRfedG9Z27dK9R9SWHulxERLKEAl1EJEtkaqDfH3QBw1B950b1nbt0r1H1JUFG9qGLiMgbZeo3dBERGUCBLiKSJdI60M1suZnVmVm9md09yPpCM/tZdP2LZlaVwtqmm9mzZrbNzLaa2acHaXOVmR0zs9ro457BflYSa9xjZpuj771+kPVmZt+Mbr9NZrY4hbXNidkutWZ23Mw+M6BNyrefmf3AzA6Z2ZaYZRPM7Ckz2xl9Hj/Ea2+NttlpZrcO1iYJtX3VzLZHf3+Pmtm4IV572n0hyTV+wcwaY36PK4Z47Wk/70ms72cxte0xs9ohXpuSbXhOIuNipt+DyK16dwEzgQJgIzBvQJuPA/8Vnb4Z+FkK65sKLI5OjwZ2DFLfVcCvAtyGe4Cy06xfATxOZJS9K4AXA/xdHyBywUSg2w94G7AY2BKz7CvA3dHpu4F7B3ndBGB39Hl8dHp8Cmq7BsiLTt87WG3x7AtJrvELwGfj2AdO+3lPVn0D1v8bcE+Q2/BcHun8Df3U4NTu3gP0D04daxXwYHT6f4GllqJh6t292d03RKdPAK8QGVs1k6wCfuQRLwDjzGxqAHUsBXa5+9leOZww7v48kXv6x4rdzx4E3j3IS68FnnL3w+5+BHgKWJ7s2tz9SXcPRWdfIDKiWGCG2H7xiOfzfs5OV180O94DPJTo902VdA70wQanHhiYrxucGugfnDqlol09i4AXB1l9pZltNLPHzezilBYGDjxpZn+KDtA9UDzbOBVuZugPUZDbr99kd2+OTh8ABhvUNh225YeJ/Mc1mOH2hWS7M9ot9IMhuqzSYfu9FTjo7juHWB/0NhxWOgd6RjCzUuDnwGfc/fiA1RuIdCMsAL4FPJbi8v7M3RcD1wGfMLO3pfj9hxUd1nAl8D+DrA56+72BR/73Trtzfc3s74EQ8JMhmgS5L3wHuABYCDQT6dZIR7dw+m/naf95SudAT/vBqc0sn0iY/8TdfzFwvbsfd/f26PQaIN/MylJVn7s3Rp8PAY8S+bc2VjzbONmuAza4+8GBK4LefjEO9ndFRZ8PDdImsG1pZrcB1wPvj/7BeYM49oWkcfeD7t7n7mHgu0O8d6D7YjQ/bgR+NlSbILdhvNI50NN6cOpof9v3gVfc/etDtJnS36dvZkuIbO+U/MExsxIzG90/TeTg2ZYBzWqAv4ye7XIFcCymayFVhvxWFOT2GyB2P7sVWD1Im7XANWY2PtqlcE10WVKZ2XLg88BKd+8cok08+0Iya4w9LnPDEO8dz+c9md4JbHf3hsFWBr0N4xb0UdnTPYichbGDyNHvv48u+yKRnRegiMi/6vXAS8DMFNb2Z0T+9d4E1EYfK4CPAR+LtrkT2ErkiP0LwJtTWN/M6PtujNbQv/1i6zPgvuj23QxUp/j3W0IkoMfGLAt0+xH549IM9BLpx72dyHGZZ4CdwNPAhGjbauB7Ma/9cHRfrAc+lKLa6on0Pffvg/1nfZ0HrDndvpDC7ffj6P61iUhITx1YY3T+DZ/3VNQXXf5A/34X0zaQbXguD136LyKSJdK5y0VERM6AAl1EJEso0EVEsoQCXUQkSyjQRUSyhAJdRCRLKNBFRLLE/wc7LhUhtxFzLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot sigmoid activated toy tensor\n",
    "plt.plot(sigmoid(A));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IpOqVYpdxgWl"
   },
   "source": [
    "Looking good! We've gone from a straight line to a curved line.\n",
    "\n",
    "Now there's plenty more [non-linear activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) that exist in PyTorch that we haven't tried.\n",
    "\n",
    "But these two are two of the most common.\n",
    "\n",
    "And the point remains, what patterns could you draw using an unlimited amount of linear (straight) and non-linear (not straight) lines?\n",
    "\n",
    "Almost anything right?\n",
    "\n",
    "That's exactly what our model is doing when we combine linear and non-linear functions.\n",
    "\n",
    "Instead of telling our model what to do, we give it tools to figure out how to best discover patterns in the data.\n",
    "\n",
    "And those tools are linear and non-linear functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1OeaW0FHC8G"
   },
   "source": [
    "## 8. Putting things together by building a multi-class PyTorch model\n",
    "\n",
    "We've covered a fair bit.\n",
    "\n",
    "But now let's put it all together using a multi-class classification problem.\n",
    "\n",
    "Recall a **binary classification** problem deals with classifying something as one of two options (e.g. a photo as a cat photo or a dog photo) where as a **multi-class classification** problem deals with classifying something from a list of *more than* two options (e.g. classifying a photo as a cat a dog or a chicken).\n",
    "\n",
    "![binary vs multi-class classification image with the example of dog vs cat for binary classification and dog vs cat vs chicken for multi-class classification](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/02-binary-vs-multi-class-classification.png)\n",
    "*Example of binary vs. multi-class classification. Binary deals with two classes (one thing or another), where as multi-class classification can deal with any number of classes over two, for example, the popular [ImageNet-1k dataset](https://www.image-net.org/) is used as a computer vision benchmark and has 1000 classes.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f5Ephtx6f1jB"
   },
   "source": [
    "### 8.1 Creating mutli-class classification data\n",
    "\n",
    "To begin a multi-class classification problem, let's create some multi-class data.\n",
    "\n",
    "To do so, we can leverage Scikit-Learn's [`make_blobs()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_blobs.html) method.\n",
    "\n",
    "This method will create however many classes (using the `centers` parameter) we want.\n",
    "\n",
    "Specifically, let's do the following:\n",
    "\n",
    "1. Create some multi-class data with `make_blobs()`.\n",
    "2. Turn the data into tensors (the default of `make_blobs()` is to use NumPy arrays).\n",
    "3. Split the data into training and test sets using `train_test_split()`.\n",
    "4. Visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "id": "x3_UwcwHHC8G",
    "outputId": "7cd92d66-41e3-4aa0-ab94-f9f9ef40fcce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-8.4134,  6.9352],\n",
      "        [-5.7665, -6.4312],\n",
      "        [-6.0421, -6.7661],\n",
      "        [ 3.9508,  0.6984],\n",
      "        [ 4.2505, -0.2815]]) tensor([3, 2, 2, 1, 1])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Set the hyperparameters for data creation\n",
    "NUM_CLASSES = 4\n",
    "NUM_FEATURES = 2\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "# 1. Create multi-class data\n",
    "X_blob, y_blob = make_blobs(n_samples=1000,\n",
    "    n_features=NUM_FEATURES, # X features\n",
    "    centers=NUM_CLASSES, # y labels \n",
    "    cluster_std=1.5, # give the clusters a little shake up (try changing this to 1.0, the default)\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 2. Turn data into tensors\n",
    "X_blob = torch.from_numpy(X_blob).type(torch.float)\n",
    "y_blob = torch.from_numpy(y_blob).type(torch.LongTensor)\n",
    "print(X_blob[:5], y_blob[:5])\n",
    "\n",
    "# 3. Split into train and test sets\n",
    "X_blob_train, X_blob_test, y_blob_train, y_blob_test = train_test_split(X_blob,\n",
    "    y_blob,\n",
    "    test_size=0.2,\n",
    "    random_state=RANDOM_SEED\n",
    ")\n",
    "\n",
    "# 4. Plot data\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.scatter(X_blob[:, 0], X_blob[:, 1], c=y_blob, cmap=plt.cm.RdYlBu);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xBCnUs0oHC8G"
   },
   "source": [
    "Nice! Looks like we've got some multi-class data ready to go.\n",
    "\n",
    "Let's build a model to separate the coloured blobs. \n",
    "\n",
    "> **Question:** Does this dataset need non-linearity? Or could you draw a succession of straight lines to separate it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_dINSA0Chiof"
   },
   "source": [
    "### 8.2 Building a multi-class classification model in PyTorch\n",
    "\n",
    "We've created a few models in PyTorch so far.\n",
    "\n",
    "You might also be starting to get an idea of how flexible neural networks are.\n",
    "\n",
    "How about we build one similar to `model_3` but this still capable of handling multi-class data?\n",
    "\n",
    "To do so, let's create a subclass of `nn.Module` that takes in three hyperparameters:\n",
    "* `input_features` - the number of `X` features coming into the model.\n",
    "* `output_features` - the ideal numbers of output features we'd like (this will be equivalent to `NUM_CLASSES` or the number of classes in your multi-class classification problem).\n",
    "* `hidden_units` - the number of hidden neurons we'd like each hidden layer to use.\n",
    "\n",
    "Since we're putting things together, let's setup some device agnostic code (we don't have to do this again in the same notebook, it's only a reminder).\n",
    "\n",
    "Then we'll create the model class using the hyperparameters above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "g9OPjDfQk1AU",
    "outputId": "64d275af-144c-4b5f-99b7-a2d2ebf235f8"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create device agnostic code\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TGoZCzOrHC8H",
    "outputId": "0f8d1d8e-e578-4bf6-eea6-1cf27bda4764"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlobModel(\n",
       "  (linear_layer_stack): Sequential(\n",
       "    (0): Linear(in_features=2, out_features=8, bias=True)\n",
       "    (1): Linear(in_features=8, out_features=8, bias=True)\n",
       "    (2): Linear(in_features=8, out_features=4, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch import nn\n",
    "\n",
    "# Build model\n",
    "class BlobModel(nn.Module):\n",
    "    def __init__(self, input_features, output_features, hidden_units=8):\n",
    "        \"\"\"Initializes all required hyperparameters for a multi-class classification model.\n",
    "\n",
    "        Args:\n",
    "            input_features (int): Number of input features to the model.\n",
    "            out_features (int): Number of output features of the model\n",
    "              (how many classes there are).\n",
    "            hidden_units (int): Number of hidden units between layers, default 8.\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        self.linear_layer_stack = nn.Sequential(\n",
    "            nn.Linear(in_features=input_features, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=hidden_units),\n",
    "            # nn.ReLU(), # <- does our dataset require non-linear layers? (try uncommenting and see if the results change)\n",
    "            nn.Linear(in_features=hidden_units, out_features=output_features), # how many classes are there?\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.linear_layer_stack(x)\n",
    "\n",
    "# Create an instance of BlobModel and send it to the target device\n",
    "model_4 = BlobModel(input_features=NUM_FEATURES, \n",
    "                    output_features=NUM_CLASSES, \n",
    "                    hidden_units=8).to(device)\n",
    "model_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_eOASXWAHC8H"
   },
   "source": [
    "Excellent! Our multi-class model is ready to go, let's create a loss function and optimizer for it.\n",
    "\n",
    "### 8.3 Creating a loss function and optimizer for a multi-class PyTorch model\n",
    "\n",
    "Since we're working on a multi-class classification problem, we'll use the `nn.CrossEntropyLoss()` method as our loss function.\n",
    "\n",
    "And we'll stick with using SGD with a learning rate of 0.1 for optimizing our `model_4` parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "3ngHLo--HC8H"
   },
   "outputs": [],
   "source": [
    "# Create loss and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_4.parameters(), \n",
    "                            lr=0.1) # exercise: try changing the learning rate here and seeing what happens to the model's performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "orcVVmLzo3gX"
   },
   "source": [
    "### 8.4 Getting prediction probabilities for a multi-class PyTorch model\n",
    "\n",
    "Alright, we've got a loss function and optimizer ready, and we're ready to train our model but before we do let's do a single forward pass with our model to see if it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "--d6YmldpZh_",
    "outputId": "2b1fb56f-cf42-49a3-f1ec-7978cfd68b56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.2711, -0.6494, -1.4740, -0.7044],\n",
       "        [ 0.2210, -1.5439,  0.0420,  1.1531],\n",
       "        [ 2.8698,  0.9143,  3.3169,  1.4027],\n",
       "        [ 1.9576,  0.3125,  2.2244,  1.1324],\n",
       "        [ 0.5458, -1.2381,  0.4441,  1.1804]], device='cuda:0',\n",
       "       grad_fn=<SliceBackward0>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform a single forward pass on the data (we'll need to put it to the target device for it to work)\n",
    "model_4(X_blob_train.to(device))[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0fk1K9VlpoPI"
   },
   "source": [
    "What's coming out here?\n",
    "\n",
    "It looks like we get one value per feature of each sample.\n",
    "\n",
    "Let's check the shape to confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3W4jnvmWp0OH",
    "outputId": "f515c93d-2c57-43fe-f5ce-d6b6aa20cd0f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4]), 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many elements in a single prediction sample?\n",
    "model_4(X_blob_train.to(device))[0].shape, NUM_CLASSES "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NyQSNaSVqBBN"
   },
   "source": [
    "Wonderful, our model is predicting one value for each class that we have.\n",
    "\n",
    "Do you remember what the raw outputs of our model are called?\n",
    "\n",
    "Hint: it rhymes with \"frog splits\" (no animals were harmed in the creation of these materials).\n",
    "\n",
    "If you guessed *logits*, you'd be correct.\n",
    "\n",
    "So right now our model is outputing logits but what if we wanted to figure out exactly which label is was giving the sample?\n",
    "\n",
    "As in, how do we go from `logits -> prediction probabilities -> prediction labels` just like we did with the binary classification problem?\n",
    "\n",
    "That's where the [softmax activation function](https://en.wikipedia.org/wiki/Softmax_function) comes into play.\n",
    "\n",
    "The softmax function calculates the probability of each prediction class being the actual predicted class compared to all other possible classes.\n",
    "\n",
    "If this doesn't make sense, let's see in code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6hU_Wxudrbiq",
    "outputId": "c12e6a9f-c80f-466c-aa5c-27e30cfe9963"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.2341, -0.3357,  0.2307,  0.2534],\n",
      "        [ 0.1198, -0.3702,  0.0998,  0.1887],\n",
      "        [ 0.3790, -0.2037,  0.4095,  0.2689],\n",
      "        [ 0.1936, -0.3733,  0.1807,  0.2496],\n",
      "        [ 0.1338, -0.1378,  0.1487,  0.0247]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n",
      "tensor([[0.2792, 0.1579, 0.2782, 0.2846],\n",
      "        [0.2729, 0.1672, 0.2675, 0.2924],\n",
      "        [0.2869, 0.1602, 0.2958, 0.2570],\n",
      "        [0.2769, 0.1571, 0.2733, 0.2928],\n",
      "        [0.2722, 0.2075, 0.2763, 0.2441]], device='cuda:0',\n",
      "       grad_fn=<SliceBackward0>)\n"
     ]
    }
   ],
   "source": [
    "# Make prediction logits with model\n",
    "y_logits = model_4(X_test.to(device))\n",
    "\n",
    "# Perform softmax calculation on logits across dimension 1 to get prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1) \n",
    "print(y_logits[:5])\n",
    "print(y_pred_probs[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A_pbSytSrzHF"
   },
   "source": [
    "Hmm, what's happened here?\n",
    "\n",
    "It may still look like the outputs of the softmax function are jumbled numbers (and they are, since our model hasn't been trained and is predicting using random patterns) but there's a very specific thing different about each sample.\n",
    "\n",
    "After passing the logits through the softmax function, each individual sample now adds to 1 (or very close to).\n",
    "\n",
    "Let's check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5fC5No7IsSiB",
    "outputId": "db517fa9-04eb-4efe-a75b-970bdf2a3163"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1., device='cuda:0', grad_fn=<SumBackward0>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the first sample output of the softmax activation function \n",
    "torch.sum(y_pred_probs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhwu9ln1sbl7"
   },
   "source": [
    "These prediction probablities are essentially saying how much the model *thinks* the target `X` sample (the input) maps to each class.\n",
    "\n",
    "Since there's one value for each class in `y_pred_probs`, the index of the *highest* value is the class the model thinks the specific data sample *most* belongs to.\n",
    "\n",
    "We can check which index has the highest value using `torch.argmax()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6X3Yf5gCsbME",
    "outputId": "a7e4db7e-08fd-426c-8b54-dfd7d3943d79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.2792, 0.1579, 0.2782, 0.2846], device='cuda:0',\n",
      "       grad_fn=<SelectBackward0>)\n",
      "tensor(3, device='cuda:0')\n"
     ]
    }
   ],
   "source": [
    "# Which class does the model think is *most* likely at the index 0 sample?\n",
    "print(y_pred_probs[0])\n",
    "print(torch.argmax(y_pred_probs[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9veE071JtSZJ"
   },
   "source": [
    "You can see the output of `torch.argmax()` returns 3, so for the features (`X`) of the sample at index 0, the model is predicting that the most likely class value (`y`) is 3.\n",
    "\n",
    "Of course, right now this is just random guessing so it's got a 25% chance of being right (since there's four classes). But we can improve those chances by training the model.\n",
    "\n",
    "> **Note:** To summarize the above, a model's raw output is referred to as **logits**.\n",
    "> \n",
    "> For a multi-class classification problem, to turn the logits into **prediction probabilities**, you use the softmax activation function (`torch.softmax`).\n",
    ">\n",
    "> The index of the value with the highest **prediction probability** is the class number the model thinks is *most* likely given the input features for that sample (although this is a prediction, it doesn't mean it will be correct)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hlqJ3_xTupU3"
   },
   "source": [
    "### 8.5 Creating a training and testing loop for a multi-class PyTorch model\n",
    "\n",
    "Alright, now we've got all of the preparation steps out of the way, let's write a training and testing loop to improve and evaluation our model.\n",
    "\n",
    "We've done many of these steps before so much of this will be practice.\n",
    "\n",
    "The only difference is that we'll be adjusting the steps to turn the model outputs (logits) to prediction probabilities (using the softmax activation function) and then to prediction labels (by taking the argmax of the output of the softmax activation function).\n",
    "\n",
    "Let's train the model for `epochs=100` and evaluate it every 10 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "55s1Pis9HC8H",
    "outputId": "4a7b0fd7-8a08-40a4-8694-435178b70832"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 1.04324, Acc: 65.50% | Test Loss: 0.57861, Test Acc: 95.50%\n",
      "Epoch: 10 | Loss: 0.14398, Acc: 99.12% | Test Loss: 0.13037, Test Acc: 99.00%\n",
      "Epoch: 20 | Loss: 0.08062, Acc: 99.12% | Test Loss: 0.07216, Test Acc: 99.50%\n",
      "Epoch: 30 | Loss: 0.05924, Acc: 99.12% | Test Loss: 0.05133, Test Acc: 99.50%\n",
      "Epoch: 40 | Loss: 0.04892, Acc: 99.00% | Test Loss: 0.04098, Test Acc: 99.50%\n",
      "Epoch: 50 | Loss: 0.04295, Acc: 99.00% | Test Loss: 0.03486, Test Acc: 99.50%\n",
      "Epoch: 60 | Loss: 0.03910, Acc: 99.00% | Test Loss: 0.03083, Test Acc: 99.50%\n",
      "Epoch: 70 | Loss: 0.03643, Acc: 99.00% | Test Loss: 0.02799, Test Acc: 99.50%\n",
      "Epoch: 80 | Loss: 0.03448, Acc: 99.00% | Test Loss: 0.02587, Test Acc: 99.50%\n",
      "Epoch: 90 | Loss: 0.03300, Acc: 99.12% | Test Loss: 0.02423, Test Acc: 99.50%\n"
     ]
    }
   ],
   "source": [
    "# Fit the model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "# Set number of epochs\n",
    "epochs = 100\n",
    "\n",
    "# Put data to target device\n",
    "X_blob_train, y_blob_train = X_blob_train.to(device), y_blob_train.to(device)\n",
    "X_blob_test, y_blob_test = X_blob_test.to(device), y_blob_test.to(device)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    ### Training\n",
    "    model_4.train()\n",
    "\n",
    "    # 1. Forward pass\n",
    "    y_logits = model_4(X_blob_train) # model outputs raw logits \n",
    "    y_pred = torch.softmax(y_logits, dim=1).argmax(dim=1) # go from logits -> prediction probabilities -> prediction labels\n",
    "    # print(y_logits)\n",
    "    # 2. Calculate loss and accuracy\n",
    "    loss = loss_fn(y_logits, y_blob_train) \n",
    "    acc = accuracy_fn(y_true=y_blob_train,\n",
    "                      y_pred=y_pred)\n",
    "\n",
    "    # 3. Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 4. Loss backwards\n",
    "    loss.backward()\n",
    "\n",
    "    # 5. Optimizer step\n",
    "    optimizer.step()\n",
    "\n",
    "    ### Testing\n",
    "    model_4.eval()\n",
    "    with torch.inference_mode():\n",
    "      # 1. Forward pass\n",
    "      test_logits = model_4(X_blob_test)\n",
    "      test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n",
    "      # 2. Calculate test loss and accuracy\n",
    "      test_loss = loss_fn(test_logits, y_blob_test)\n",
    "      test_acc = accuracy_fn(y_true=y_blob_test,\n",
    "                             y_pred=test_pred)\n",
    "\n",
    "    # Print out what's happening\n",
    "    if epoch % 10 == 0:\n",
    "        print(f\"Epoch: {epoch} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test Loss: {test_loss:.5f}, Test Acc: {test_acc:.2f}%\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m_JNlpd4L6dL"
   },
   "source": [
    "### 8.6 Making and evaluating predictions with a PyTorch multi-class model\n",
    "\n",
    "It looks like our trained model is performaning pretty well.\n",
    "\n",
    "But to make sure of this, let's make some predictions and visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NjCKKhsGHC8H",
    "outputId": "583e10fb-fa1c-463a-fbd0-4d31d76b82ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[  4.3377,  10.3539, -14.8948,  -9.7642],\n",
       "        [  5.0142, -12.0371,   3.3860,  10.6699],\n",
       "        [ -5.5885, -13.3448,  20.9894,  12.7711],\n",
       "        [  1.8400,   7.5599,  -8.6016,  -6.9942],\n",
       "        [  8.0726,   3.2906, -14.5998,  -3.6186],\n",
       "        [  5.5844, -14.9521,   5.0168,  13.2890],\n",
       "        [ -5.9739, -10.1913,  18.8655,   9.9179],\n",
       "        [  7.0755,  -0.7601,  -9.5531,   0.1736],\n",
       "        [ -5.5918, -18.5990,  25.5309,  17.5799],\n",
       "        [  7.3142,   0.7197, -11.2017,  -1.2011]], device='cuda:0')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "model_4.eval()\n",
    "with torch.inference_mode():\n",
    "    y_logits = model_4(X_blob_test)\n",
    "\n",
    "# View the first 10 predictions\n",
    "y_logits[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lpAdeJRMNHjG"
   },
   "source": [
    "Alright, looks like our model's predictions are still in logit form.\n",
    "\n",
    "Though to evaluate them, they'll have to be in the same form as our labels (`y_blob_test`) which are in integer form.\n",
    "\n",
    "Let's convert our model's prediction logits to prediction probabilities (using `torch.softmax()`) then to prediction labels (by taking the `argmax()` of each sample).\n",
    "\n",
    "> **Note:** It's possible to skip the `torch.softmax()` function and go straight from `predicted logits -> predicted labels` by calling `torch.argmax()` directly on the logits.\n",
    ">\n",
    "> For example, `y_preds = torch.argmax(y_logits, dim=1)`, this saves a computation step (no `torch.softmax()`) but results in no prediction probabilities being available to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "faDQ4oLpHC8H",
    "outputId": "ca32986f-8dc0-419d-84df-1cc3ba8577e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Labels: tensor([1, 3, 2, 1, 0, 3, 2, 0, 2, 0], device='cuda:0')\n",
      "Test accuracy: 99.5%\n"
     ]
    }
   ],
   "source": [
    "# Turn predicted logits in prediction probabilities\n",
    "y_pred_probs = torch.softmax(y_logits, dim=1)\n",
    "\n",
    "# Turn prediction probabilities into prediction labels\n",
    "y_preds = y_pred_probs.argmax(dim=1)\n",
    "\n",
    "# Compare first 10 model preds and test labels\n",
    "print(f\"Predictions: {y_preds[:10]}\\nLabels: {y_blob_test[:10]}\")\n",
    "print(f\"Test accuracy: {accuracy_fn(y_true=y_blob_test, y_pred=y_preds)}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AMA5SSixOSru"
   },
   "source": [
    "Nice! Our model predictions are now in the same form as our test labels.\n",
    "\n",
    "Let's visualize them with `plot_decision_boundary()`, remember because our data is on the GPU, we'll have to move it to the CPU for use with matplotlib (`plot_decision_boundary()` does this automatically for us)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "id": "kLOzBFdRHC8I",
    "outputId": "71dcaf7f-a30e-457d-8949-916cf9c5cc79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Train\")\n",
    "plot_decision_boundary(model_4, X_blob_train, y_blob_train)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Test\")\n",
    "plot_decision_boundary(model_4, X_blob_test, y_blob_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p0vFchUQx7mc"
   },
   "source": [
    "## 9. More classification evaluation metrics\n",
    "\n",
    "So far we've only covered a couple of ways of evaluating a classification model (accuracy, loss and visualizing predictions).\n",
    "\n",
    "These are some of the most common methods you'll come across and are a good starting point.\n",
    "\n",
    "However, you may want to evaluate you classification model using more metrics such as the following:\n",
    "\n",
    "| **Metric name/Evaluation method** | **Defintion** | **Code** |\n",
    "| --- | --- | --- |\n",
    "| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | [`torchmetrics.Accuracy()`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#id3) or [`sklearn.metrics.accuracy_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html) |\n",
    "| Precision | Proportion of true positives over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should've been 0). | [`torchmetrics.Precision()`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#id4) or [`sklearn.metrics.precision_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) |\n",
    "| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should've been 1). Higher recall leads to less false negatives. | [`torchmetrics.Recall()`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#id5) or [`sklearn.metrics.recall_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html) |\n",
    "| F1-score | Combines precision and recall into one metric. 1 is best, 0 is worst. | [`torchmetrics.F1Score()`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#f1score) or [`sklearn.metrics.f1_score()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) |\n",
    "| [Confusion matrix](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)  | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line). | [`torchmetrics.ConfusionMatrix`](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#confusionmatrix) or [`sklearn.metrics.plot_confusion_matrix()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay.from_predictions) |\n",
    "| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | [`sklearn.metrics.classification_report()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) |\n",
    "\n",
    "Scikit-Learn (a popular and world-class machine learning library) has many implementations of the above metrics and you're looking for a PyTorch-like version, check out [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/), especially the [TorchMetrics classification section](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#classification). \n",
    "\n",
    "Let's try the `torchmetrics.Accuracy` metric out.\n",
    "\n",
    "\n"
   ]
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      "\u001b[K     |████████████████████████████████| 409 kB 5.2 MB/s eta 0:00:01\n",
      "\u001b[?25h"
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   "source": [
    "!pip -q install torchmetrics\n",
    "\n",
    "from torchmetrics import Accuracy\n",
    "\n",
    "# Setup metric and make sure it's on the target device\n",
    "torchmetrics_accuracy = Accuracy().to(device)\n",
    "\n",
    "# Calculate accuracy\n",
    "torchmetrics_accuracy(y_preds, y_blob_test)"
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    "## Exercises\n",
    "\n",
    "All of the exercises are focused on practicing the code in the sections above.\n",
    "\n",
    "You should be able to complete them by referencing each section or by following the resource(s) linked.\n",
    "\n",
    "All exercises should be completed using [device-agonistic code](https://pytorch.org/docs/stable/notes/cuda.html#device-agnostic-code).\n",
    "\n",
    "Resources:\n",
    "* [Exercise template notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/exercises/02_pytorch_classification_exercises.ipynb)\n",
    "* [Example solutions notebook for 02](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/solutions/02_pytorch_classification_exercise_solutions.ipynb) (try the exercises *before* looking at this)\n",
    "\n",
    "1. Make a binary classification dataset with Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function.\n",
    "  * For consistency, the dataset should have 1000 samples and a `random_state=42`.\n",
    "  * Turn the data into PyTorch tensors. Split the data into training and test sets using `train_test_split` with 80% training and 20% testing.\n",
    "2. Build a model by subclassing `nn.Module` that incorporates non-linear activation functions and is capable of fitting the data you created in 1.\n",
    "  * Feel free to use any combination of PyTorch layers (linear and non-linear) you want.\n",
    "3. Setup a binary classification compatible loss function and optimizer to use when training the model.\n",
    "4. Create a training and testing loop to fit the model you created in 2 to the data you created in 1.\n",
    "  * To measure model accuray, you can create your own accuracy function or use the accuracy function in [TorchMetrics](https://torchmetrics.readthedocs.io/en/latest/).\n",
    "  * Train the model for long enough for it to reach over 96% accuracy.\n",
    "  * The training loop should output progress every 10 epochs of the model's training and test set loss and accuracy.\n",
    "5. Make predictions with your trained model and plot them using the `plot_decision_boundary()` function created in this notebook.\n",
    "6. Replicate the Tanh (hyperbolic tangent) activation function in pure PyTorch.\n",
    "  * Feel free to reference the [ML cheatsheet website](https://ml-cheatsheet.readthedocs.io/en/latest/activation_functions.html#tanh) for the formula.\n",
    "7. Create a multi-class dataset using the [spirals data creation function from CS231n](https://cs231n.github.io/neural-networks-case-study/\n",
    " ) (see below for the code).\n",
    "  * Construct a model capable of fitting the data (you may need a combination of linear and non-linear layers).\n",
    "  * Build a loss function and optimizer capable of handling multi-class data (optional extension: use the Adam optimizer instead of SGD, you may have to experiment with different values of the learning rate to get it working).\n",
    "  * Make a training and testing loop for the multi-class data and train a model on it to reach over 95% testing accuracy (you can use any accuracy measuring function here that you like).\n",
    "  * Plot the decision boundaries on the spirals dataset from your model predictions, the `plot_decision_boundary()` function should work for this dataset too.\n",
    "\n",
    "```python\n",
    "# Code for creating a spiral dataset from CS231n\n",
    "import numpy as np\n",
    "N = 100 # number of points per class\n",
    "D = 2 # dimensionality\n",
    "K = 3 # number of classes\n",
    "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n",
    "y = np.zeros(N*K, dtype='uint8') # class labels\n",
    "for j in range(K):\n",
    "  ix = range(N*j,N*(j+1))\n",
    "  r = np.linspace(0.0,1,N) # radius\n",
    "  t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta\n",
    "  X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
    "  y[ix] = j\n",
    "# lets visualize the data\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "## Extra-curriculum\n",
    "\n",
    "* Write down 3 problems where you think machine classification could be useful (these can be anything, get creative as you like, for example, classifying credit card transactions as fraud or not fraud based on the purchase amount and purchase location features). \n",
    "* Research the concept of \"momentum\" in gradient-based optimizers (like SGD or Adam), what does it mean?\n",
    "* Spend 10-minutes reading the [Wikipedia page for different activation functions](https://en.wikipedia.org/wiki/Activation_function#Table_of_activation_functions), how many of these can you line up with [PyTorch's activation functions](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity)?\n",
    "* Research when accuracy might be a poor metric to use (hint: read [\"Beyond Accuracy\" by by Will Koehrsen](https://willkoehrsen.github.io/statistics/learning/beyond-accuracy-precision-and-recall/) for ideas).\n",
    "* **Watch:** For an idea of what's happening within our neural networks and what they're doing to learn, watch [MIT's Introduction to Deep Learning video](https://youtu.be/7sB052Pz0sQ)."
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